diff --git a/en/数学进阶/convex/index.html b/en/数学进阶/convex/index.html index 1299e8c1..88860721 100644 --- a/en/数学进阶/convex/index.html +++ b/en/数学进阶/convex/index.html @@ -1,2 +1,2 @@ - Standford EE364A: Convex Optimization - CSDIY.wiki
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Stanford EE364A: Convex Optimization

课程简介

  • 所属大学：Stanford
  • 先修要求：Python，微积分，线性代数，概率论，数值分析
  • 编程语言：Python
  • 课程难度：🌟🌟🌟🌟🌟
  • 预计学时：150 小时

Stephen Boyd 教授是凸优化领域的大牛，其编写的 Convex Optimization 这本教材被众多名校采用。另外其研究团队还专门开发了一个用于求解常见凸优化问题的编程框架，支持 Python, Julia 等主流编程语言，其课程作业也是采用这个编程框架去解决实际生活当中的凸优化问题。

在实际运用当中，你会深刻体会到对于同一个问题，建模过程中一个细小的改变，其方程的求解难度会有天壤之别，如何让你建模的方程是“凸”的，是一门艺术。

课程资源

资源汇总

@PKUFlyingPig 在学习这门课中用到的所有资源和作业实现都汇总在 PKUFlyingPig/Standford_CVX101 - GitHub 中。

Last update: September 6, 2022

Stanford EE364A: Convex Optimization

Descriptions

  • Offered by: Stanford
  • Prerequisites: Python, Calculus, Linear Algebra, Probability Theory, Numerical Analysis
  • Programming Languages: Python
  • Difficulty: 🌟🌟🌟🌟🌟
  • Class Hour: 150 hours

Professor Stephen Boyd is a great expert in the field of convex optimization and his textbook Convex Optimization has been adopted by many prestigious universities. His team has also developed a programming framework for solving common convex optimization problems in Python, Julia, and other popular programming languages, and its homework assignments also use this programming framework to solve real-life convex optimization problems.

In practice, you will deeply understand that for the same problem, a small change in the modeling process can make a world of difference in the difficulty of solving the equation. It is an art to make the equations you formulate "convex".

Course Resources

Personal Resources

All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPic/Standford_CVX101 - GitHub

Last update: September 29, 2022
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Pull Request\uff0c\u4e5f\u6b22\u8fce\u548c\u6211\u90ae\u4ef6\u8054\u7cfb\uff08 zhongyinmin@pku.edu.cn \uff09\u3002","title":"\u4f60\u4e5f\u60f3\u52a0\u5165\u5230\u8d21\u732e\u8005\u7684\u884c\u5217"},{"location":"#_8","text":"\u65b9\u6cd5\u53c2\u89c1\u4ed3\u5e93\u7684 README.md \u3002","title":"\u5173\u4e8e\u4ea4\u6d41\u7fa4\u7684\u5efa\u7acb"},{"location":"CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/","text":"\u4e00\u4e2a\u4ec5\u4f9b\u53c2\u8003\u7684 CS \u5b66\u4e60\u89c4\u5212 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Calculus Course \u548c 18.06: Linear Algebra \u7684\u8bfe\u7a0b notes\uff0c\u81f3\u5c11\u4e8e\u6211\u800c\u8a00\uff0c\u5b83\u5e2e\u52a9\u6211\u6df1\u523b\u7406\u89e3\u4e86\u5fae\u79ef\u5206\u548c\u7ebf\u6027\u4ee3\u6570\u7684\u8bb8\u591a\u672c\u8d28\u3002\u987a\u9053\u518d\u5b89\u5229\u4e00\u4e2a\u6cb9\u7ba1\u6570\u5b66\u7f51\u7ea2 3Blue1Brown \uff0c\u4ed6\u7684\u9891\u9053\u6709\u5f88\u591a\u7528\u751f\u52a8\u5f62\u8c61\u7684\u52a8\u753b\u9610\u91ca\u6570\u5b66\u672c\u8d28\u5185\u6838\u7684\u89c6\u9891\uff0c\u517c\u5177\u6df1\u5ea6\u548c\u5e7f\u5ea6\uff0c\u8d28\u91cf\u975e\u5e38\u9ad8\u3002 \u4fe1\u606f\u8bba\u5165\u95e8 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u53ca\u65e9\u4e86\u89e3\u4e00\u4e9b\u4fe1\u606f\u8bba\u7684\u57fa\u7840\u77e5\u8bc6\uff0c\u6211\u89c9\u5f97\u662f\u5927\u6709\u88e8\u76ca\u7684\u3002\u4f46\u5927\u591a\u4fe1\u606f\u8bba\u8bfe\u7a0b\u90fd\u9762\u5411\u9ad8\u5e74\u7ea7\u672c\u79d1\u751f\u751a\u81f3\u7814\u7a76\u751f\uff0c\u5bf9\u65b0\u624b\u6781\u4e0d\u53cb\u597d\u3002\u800c MIT \u7684 6.050J: Information theory and Entropy \u8fd9\u95e8\u8bfe\u6b63\u662f\u4e3a\u5927\u4e00\u65b0\u751f\u91cf\u8eab\u5b9a\u5236\u7684\uff0c\u51e0\u4e4e\u6ca1\u6709\u5148\u4fee\u8981\u6c42\uff0c\u6db5\u76d6\u4e86\u7f16\u7801\u3001\u538b\u7f29\u3001\u901a\u4fe1\u3001\u4fe1\u606f\u71b5\u7b49\u7b49\u5185\u5bb9\uff0c\u975e\u5e38\u6709\u8da3\u3002 \u6570\u5b66\u8fdb\u9636 \u79bb\u6563\u6570\u5b66\u4e0e\u6982\u7387\u8bba \u96c6\u5408\u8bba\u3001\u56fe\u8bba\u3001\u6982\u7387\u8bba\u7b49\u7b49\u662f\u7b97\u6cd5\u63a8\u5bfc\u4e0e\u8bc1\u660e\u7684\u91cd\u8981\u5de5\u5177\uff0c\u4e5f\u662f\u540e\u7eed\u9ad8\u9636\u6570\u5b66\u8bfe\u7a0b\u7684\u57fa\u7840\u3002\u4f46\u6211\u89c9\u5f97\u8fd9\u7c7b\u8bfe\u7a0b\u7684\u8bb2\u6388\u5f88\u5bb9\u6613\u843d\u5165\u7406\u8bba\u5316\u4e0e\u5f62\u5f0f\u5316\u7684\u7aa0\u81fc\uff0c\u8ba9\u8bfe\u5802\u6210\u4e3a\u5b9a\u7406\u7ed3\u8bba\u7684\u5806\u780c\uff0c\u800c\u65e0\u6cd5\u4f7f\u5b66\u751f\u6df1\u523b\u628a\u63e1\u7406\u8bba\u7684\u672c\u8d28\uff0c\u8fdb\u800c\u9020\u6210\u5b66\u4e86\u5c31\u80cc\uff0c\u8003\u4e86\u5c31\u5fd8\u7684\u602a\u5708\u3002\u5982\u679c\u80fd\u5728\u7406\u8bba\u6559\u5b66\u4e2d\u7a7f\u63d2\u7b97\u6cd5\u8fd0\u7528\u5b9e\u4f8b\uff0c\u5b66\u751f\u5728\u62d3\u5c55\u7b97\u6cd5\u77e5\u8bc6\u7684\u540c\u65f6\u4e5f\u80fd\u7aa5\u89c1\u7406\u8bba\u7684\u529b\u91cf\u548c\u9b45\u529b\u3002 UCB CS70 : discrete Math and probability theory \u548c UCB CS126 : Probability theory \u662f UC Berkeley \u7684\u6982\u7387\u8bba\u8bfe\u7a0b\uff0c\u524d\u8005\u8986\u76d6\u4e86\u79bb\u6563\u6570\u5b66\u548c\u6982\u7387\u8bba\u57fa\u7840\uff0c\u540e\u8005\u5219\u6d89\u53ca\u968f\u673a\u8fc7\u7a0b\u4ee5\u53ca\u6df1\u5165\u7684\u7406\u8bba\u5185\u5bb9\u3002\u4e24\u8005\u90fd\u975e\u5e38\u6ce8\u91cd\u7406\u8bba\u548c\u5b9e\u8df5\u7684\u7ed3\u5408\uff0c\u6709\u4e30\u5bcc\u7684\u7b97\u6cd5\u5b9e\u9645\u8fd0\u7528\u5b9e\u4f8b\uff0c\u540e\u8005\u8fd8\u6709\u5927\u91cf\u7684 Python \u7f16\u7a0b\u4f5c\u4e1a\u6765\u8ba9\u5b66\u751f\u8fd0\u7528\u6982\u7387\u8bba\u7684\u77e5\u8bc6\u89e3\u51b3\u5b9e\u9645\u95ee\u9898\u3002 \u6570\u503c\u5206\u6790 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u57f9\u517b\u8ba1\u7b97\u601d\u7ef4\u662f\u5f88\u91cd\u8981\u7684\uff0c\u5b9e\u9645\u95ee\u9898\u7684\u5efa\u6a21\u3001\u79bb\u6563\u5316\uff0c\u8ba1\u7b97\u673a\u7684\u6a21\u62df\u3001\u5206\u6790\uff0c\u662f\u4e00\u9879\u5f88\u91cd\u8981\u7684\u80fd\u529b\u3002\u800c\u8fd9\u4e24\u5e74\u5f00\u59cb\u98ce\u9761\u7684\uff0c\u7531 MIT \u6253\u9020\u7684 Julia \u7f16\u7a0b\u8bed\u8a00\u4ee5\u5176 C \u4e00\u6837\u7684\u901f\u5ea6\u548c Python \u4e00\u6837\u53cb\u597d\u7684\u8bed\u6cd5\u5728\u6570\u503c\u8ba1\u7b97\u9886\u57df\u6709\u4e00\u7edf\u5929\u4e0b\u4e4b\u52bf\uff0cMIT \u7684\u8bb8\u591a\u6570\u5b66\u8bfe\u7a0b\u4e5f\u5f00\u59cb\u7528 Julia \u4f5c\u4e3a\u6559\u5b66\u5de5\u5177\uff0c\u628a\u8270\u6df1\u7684\u6570\u5b66\u7406\u8bba\u7528\u76f4\u89c2\u6e05\u6670\u7684\u4ee3\u7801\u5c55\u793a\u51fa\u6765\u3002 ComputationalThinking \u662f MIT \u5f00\u8bbe\u7684\u4e00\u95e8\u8ba1\u7b97\u601d\u7ef4\u5165\u95e8\u8bfe\uff0c\u6240\u6709\u8bfe\u7a0b\u5185\u5bb9\u5168\u90e8\u5f00\u6e90\uff0c\u53ef\u4ee5\u5728\u8bfe\u7a0b\u7f51\u7ad9\u76f4\u63a5\u8bbf\u95ee\u3002\u8fd9\u95e8\u8bfe\u5229\u7528 Julia \u7f16\u7a0b\u8bed\u8a00\uff0c\u5728\u56fe\u50cf\u5904\u7406\u3001\u793e\u4f1a\u79d1\u5b66\u4e0e\u6570\u636e\u79d1\u5b66\u3001\u6c14\u5019\u5b66\u5efa\u6a21\u4e09\u4e2a topic \u4e0b\u5e26\u9886\u5b66\u751f\u7406\u89e3\u7b97\u6cd5\u3001\u6570\u5b66\u5efa\u6a21\u3001\u6570\u636e\u5206\u6790\u3001\u4ea4\u4e92\u8bbe\u8ba1\u3001\u56fe\u4f8b\u5c55\u793a\uff0c\u8ba9\u5b66\u751f\u4f53\u9a8c\u8ba1\u7b97\u4e0e\u79d1\u5b66\u7684\u7f8e\u5999\u7ed3\u5408\u3002\u5185\u5bb9\u867d\u7136\u4e0d\u96be\uff0c\u4f46\u7ed9\u6211\u6700\u6df1\u523b\u7684\u611f\u53d7\u5c31\u662f\uff0c\u79d1\u5b66\u7684\u9b45\u529b\u5e76\u4e0d\u662f\u6545\u5f04\u7384\u865a\u7684\u8270\u6df1\u7406\u8bba\uff0c\u4e0d\u662f\u8bd8\u5c48\u8071\u7259\u7684\u672f\u8bed\u884c\u8bdd\uff0c\u800c\u662f\u7528\u76f4\u89c2\u751f\u52a8\u7684\u6848\u4f8b\uff0c\u7528\u7b80\u7ec3\u6df1\u523b\u7684\u8bed\u8a00\uff0c\u8ba9\u6bcf\u4e2a\u666e\u901a\u4eba\u90fd\u80fd\u7406\u89e3\u3002 \u4e0a\u5b8c\u4e0a\u9762\u7684\u4f53\u9a8c\u8bfe\u4e4b\u540e\uff0c\u5982\u679c\u610f\u72b9\u672a\u5c3d\u7684\u8bdd\uff0c\u4e0d\u59a8\u8bd5\u8bd5 MIT \u7684 18.330 : Introduction to numerical analysis \uff0c\u8fd9\u95e8\u8bfe\u7684\u7f16\u7a0b\u4f5c\u4e1a\u540c\u6837\u4f1a\u7528 Julia \u7f16\u7a0b\u8bed\u8a00\uff0c\u4e0d\u8fc7\u96be\u5ea6\u548c\u6df1\u5ea6\u4e0a\u90fd\u4e0a\u4e86\u4e00\u4e2a\u53f0\u9636\u3002\u5185\u5bb9\u6d89\u53ca\u4e86\u6d6e\u70b9\u7f16\u7801\u3001Root finding\u3001\u7ebf\u6027\u7cfb\u7edf\u3001\u5fae\u5206\u65b9\u7a0b\u7b49\u7b49\u65b9\u9762\uff0c\u6574\u95e8\u8bfe\u7684\u4e3b\u65e8\u5c31\u662f\u8ba9\u4f60\u5229\u7528\u79bb\u6563\u5316\u7684\u8ba1\u7b97\u673a\u8868\u793a\u53bb\u4f30\u8ba1\u548c\u903c\u8fd1\u4e00\u4e2a\u6570\u5b66\u4e0a\u8fde\u7eed\u7684\u6982\u5ff5\u3002\u8fd9\u95e8\u8bfe\u7684\u6559\u6388\u8fd8\u4e13\u95e8\u64b0\u5199\u4e86\u4e00\u672c\u914d\u5957\u7684\u5f00\u6e90\u6559\u6750 Fundamentals of Numerical Computation \uff0c\u91cc\u9762\u9644\u6709\u4e30\u5bcc\u7684 Julia \u4ee3\u7801\u5b9e\u4f8b\u548c\u4e25\u8c28\u7684\u516c\u5f0f\u63a8\u5bfc\u3002 \u5982\u679c\u4f60\u8fd8\u610f\u72b9\u672a\u5c3d\u7684\u8bdd\uff0c\u8fd8\u6709 MIT \u7684\u6570\u503c\u5206\u6790\u7814\u7a76\u751f\u8bfe\u7a0b 18.335: Introduction to numerical method \u4f9b\u4f60\u53c2\u8003\u3002 \u5fae\u5206\u65b9\u7a0b \u5982\u679c\u4e16\u95f4\u4e07\u7269\u7684\u8fd0\u52a8\u53d1\u5c55\u90fd\u80fd\u7528\u65b9\u7a0b\u6765\u523b\u753b\u548c\u63cf\u8ff0\uff0c\u8fd9\u662f\u4e00\u4ef6\u591a\u4e48\u9177\u7684\u4e8b\u60c5\u5440\uff01\u867d\u7136\u51e0\u4e4e\u4efb\u4f55\u4e00\u6240\u5b66\u6821\u7684 CS \u57f9\u517b\u65b9\u6848\u4e2d\u90fd\u6ca1\u6709\u5fae\u5206\u65b9\u7a0b\u76f8\u5173\u7684\u5fc5\u4fee\u8bfe\u7a0b\uff0c\u4f46\u6211\u8fd8\u662f\u89c9\u5f97\u638c\u63e1\u5b83\u4f1a\u8d4b\u4e88\u4f60\u4e00\u4e2a\u65b0\u7684\u89c6\u89d2\u6765\u5ba1\u89c6\u8fd9\u4e2a\u4e16\u754c\u3002 \u7531\u4e8e\u5fae\u5206\u65b9\u7a0b\u4e2d\u5f80\u5f80\u4f1a\u7528\u5230\u5f88\u591a\u590d\u53d8\u51fd\u6570\u7684\u77e5\u8bc6\uff0c\u6240\u4ee5\u5927\u5bb6\u53ef\u4ee5\u53c2\u8003 MIT18.04: Complex variables functions \u7684\u8bfe\u7a0b notes \u6765\u8865\u9f50\u5148\u4fee\u77e5\u8bc6\u3002 MIT18.03: differential equations ) \u4e3b\u8981\u8986\u76d6\u4e86\u5e38\u5fae\u5206\u65b9\u7a0b\u7684\u6c42\u89e3\uff0c\u5728\u6b64\u57fa\u7840\u4e4b\u4e0a MIT18.152: Partial differential equations ) \u5219\u4f1a\u6df1\u5165\u504f\u5fae\u5206\u65b9\u7a0b\u7684\u5efa\u6a21\u4e0e\u6c42\u89e3\u3002\u638c\u63e1\u4e86\u5fae\u5206\u65b9\u7a0b\u8fd9\u4e00\u6709\u5229\u5de5\u5177\uff0c\u76f8\u4fe1\u5bf9\u4e8e\u4f60\u7684\u5b9e\u9645\u95ee\u9898\u7684\u5efa\u6a21\u80fd\u529b\u4ee5\u53ca\u4ece\u4f17\u591a\u566a\u58f0\u53d8\u91cf\u4e2d\u628a\u63e1\u672c\u8d28\u7684\u76f4\u89c9\u90fd\u4f1a\u6709\u5f88\u5927\u5e2e\u52a9\u3002 \u6570\u5b66\u9ad8\u9636 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u6211\u7ecf\u5e38\u542c\u5230\u6570\u5b66\u65e0\u7528\u8bba\u7684\u8bba\u65ad\uff0c\u5bf9\u6b64\u6211\u4e0d\u6562\u82df\u540c\u4f46\u4e5f\u65e0\u6743\u53cd\u5bf9\uff0c\u4f46\u82e5\u51e1\u4e8b\u90fd\u786c\u8981\u4e89\u51fa\u4e2a\u6709\u7528\u548c\u65e0\u7528\u7684\u533a\u522b\u6765\uff0c\u5012\u4e5f\u7740\u5b9e\u65e0\u8da3\uff0c\u56e0\u6b64\u4e0b\u9762\u8fd9\u4e9b\u9762\u5411\u9ad8\u5e74\u7ea7\u751a\u81f3\u7814\u7a76\u751f\u7684\u6570\u5b66\u8bfe\u7a0b\uff0c\u5927\u5bb6\u6309\u5174\u8da3\u81ea\u53d6\u6240\u9700\u3002 \u51f8\u4f18\u5316 Standford EE364A: Convex Optimization \u4fe1\u606f\u8bba MIT6.441: Information Theory \u5e94\u7528\u7edf\u8ba1\u5b66 MIT18.650: Statistics for Applications \u521d\u7b49\u6570\u8bba MIT18.781: Theory of Numbers \u5bc6\u7801\u5b66 Standford CS255: Cryptography \u7f16\u7a0b\u5165\u95e8 Languages are tools, you choose the right tool to do the right thing. Since there's no universally perfect tool, there's no universally perfect language. Shell MIT-Missing-Semester Python Harvard CS50: This is CS50x UCB CS61A: Structure and Interpretation of Computer Programs C++ Stanford CS106B/X: Programming Abstractions Stanford CS106L: Standard C++ Programming Rust Stanford CS110L: Safety in Systems Programming OCaml Cornell CS3110 textbook: Functional Programming in OCaml \u7535\u5b50\u57fa\u7840 \u7535\u8def\u57fa\u7840 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u4e86\u89e3\u4e00\u4e9b\u57fa\u7840\u7684\u7535\u8def\u77e5\u8bc6\uff0c\u611f\u53d7\u4ece\u4f20\u611f\u5668\u6536\u96c6\u6570\u636e\u5230\u6570\u636e\u5206\u6790\u518d\u5230\u7b97\u6cd5\u9884\u6d4b\u6574\u6761\u6d41\u6c34\u7ebf\uff0c\u5bf9\u4e8e\u540e\u7eed\u77e5\u8bc6\u7684\u5b66\u4e60\u4ee5\u53ca\u8ba1\u7b97\u601d\u7ef4\u7684\u57f9\u517b\u8fd8\u662f\u5f88\u6709\u5e2e\u52a9\u7684\u3002 EE16A&B: Designing Information Devices and Systems I&II \u662f\u4f2f\u514b\u5229 EE \u5b66\u751f\u7684\u5927\u4e00\u5165\u95e8\u8bfe\uff0c\u5176\u4e2d EE16A \u6ce8\u91cd\u901a\u8fc7\u7535\u8def\u4ece\u5b9e\u9645\u73af\u5883\u4e2d\u6536\u96c6\u548c\u5206\u6790\u6570\u636e\uff0c\u800c EE16B \u5219\u4fa7\u91cd\u4ece\u8fd9\u4e9b\u6536\u96c6\u5230\u7684\u6570\u636e\u8fdb\u884c\u5206\u6790\u5e76\u505a\u51fa\u9884\u6d4b\u884c\u4e3a\u3002 \u4fe1\u53f7\u4e0e\u7cfb\u7edf \u4fe1\u53f7\u4e0e\u7cfb\u7edf\u662f\u4e00\u95e8\u6211\u89c9\u5f97\u975e\u5e38\u503c\u5f97\u4e00\u4e0a\u7684\u8bfe\uff0c\u6700\u521d\u5b66\u5b83\u53ea\u662f\u4e3a\u4e86\u6ee1\u8db3\u6211\u5bf9\u5085\u91cc\u53f6\u53d8\u6362\u7684\u597d\u5947\uff0c\u4f46\u5b66\u5b8c\u4e4b\u540e\u6211\u624d\u4e0d\u7981\u611f\u53f9\uff0c\u5085\u7acb\u53f6\u53d8\u6362\u7ed9\u6211\u63d0\u4f9b\u4e86\u4e00\u4e2a\u5168\u65b0\u7684\u89c6\u89d2\u53bb\u770b\u5f85\u8fd9\u4e2a\u4e16\u754c\uff0c\u5c31\u5982\u540c\u5fae\u5206\u65b9\u7a0b\u4e00\u6837\uff0c\u8ba9\u4f60\u6c89\u6d78\u5728\u7528\u6570\u5b66\u53bb\u7cbe\u786e\u63cf\u7ed8\u548c\u523b\u753b\u8fd9\u4e2a\u4e16\u754c\u7684\u4f18\u96c5\u4e0e\u795e\u5947\u4e4b\u4e2d\u3002 MIT 6.003: signal and systems \u63d0\u4f9b\u4e86\u5168\u90e8\u7684\u8bfe\u7a0b\u5f55\u5f71\u3001\u4e66\u9762\u4f5c\u4e1a\u4ee5\u53ca\u7b54\u6848\u3002\u4e5f\u53ef\u4ee5\u53bb\u770b\u8fd9\u95e8\u8bfe\u7684 \u8fdc\u53e4\u7248\u672c \u800c UCB EE120: Signal and Systems \u5173\u4e8e\u5085\u7acb\u53f6\u53d8\u6362\u7684 notes \u5199\u5f97\u975e\u5e38\u597d\uff0c\u5e76\u4e14\u63d0\u4f9b\u4e866 \u4e2a\u975e\u5e38\u6709\u8da3\u7684 Python \u7f16\u7a0b\u4f5c\u4e1a\uff0c\u8ba9\u4f60\u5b9e\u8df5\u4e2d\u8fd0\u7528\u4fe1\u53f7\u4e0e\u7cfb\u7edf\u7684\u7406\u8bba\u4e0e\u7b97\u6cd5\u3002 \u6570\u636e\u7ed3\u6784\u4e0e\u7b97\u6cd5 \u6570\u636e\u7ed3\u6784\u4e0e\u7b97\u6cd5 UCB CS61B: Data Structures and Algorithms Coursera: Algorithms I & II \u7b97\u6cd5\u8bbe\u8ba1\u4e0e\u5206\u6790 UCB CS170: Efficient Algorithms and Intractable Problems \u8f6f\u4ef6\u5de5\u7a0b \u5165\u95e8\u8bfe \u4e00\u4efd\u201c\u80fd\u8dd1\u201d\u7684\u4ee3\u7801\uff0c\u548c\u4e00\u4efd\u9ad8\u8d28\u91cf\u7684\u5de5\u4e1a\u7ea7\u4ee3\u7801\u662f\u6709\u672c\u8d28\u533a\u522b\u7684\u3002\u56e0\u6b64\u6211\u975e\u5e38\u63a8\u8350\u4f4e\u5e74\u7ea7\u7684\u540c\u5b66\u5b66\u4e60\u4e00\u4e0b MIT 6.031: Software Construction \u8fd9\u95e8\u8bfe\uff0c\u5b83\u4f1a\u4ee5 Java \u8bed\u8a00\u4e3a\u57fa\u7840\uff0c\u4ee5\u4e30\u5bcc\u7ec6\u81f4\u7684\u9605\u8bfb\u6750\u6599\u548c\u7cbe\u5fc3\u8bbe\u8ba1\u7684\u7f16\u7a0b\u7ec3\u4e60\u4f20\u6388\u5982\u4f55\u7f16\u5199 \u4e0d\u6613\u51fa bug\u3001\u7b80\u660e\u6613\u61c2\u3001\u6613\u4e8e\u7ef4\u62a4\u4fee\u6539 \u7684\u9ad8\u8d28\u91cf\u4ee3\u7801\u3002\u5927\u5230\u5b8f\u89c2\u6570\u636e\u7ed3\u6784\u8bbe\u8ba1\uff0c\u5c0f\u5230\u5982\u4f55\u5199\u6ce8\u91ca\uff0c\u9075\u5faa\u8fd9\u4e9b\u524d\u4eba\u603b\u7ed3\u7684\u7ec6\u8282\u548c\u7ecf\u9a8c\uff0c\u5bf9\u4e8e\u4f60\u6b64\u540e\u7684\u7f16\u7a0b\u751f\u6daf\u5927\u6709\u88e8\u76ca\u3002 \u4e13\u4e1a\u8bfe \u5f53\u7136\uff0c\u5982\u679c\u4f60\u60f3\u7cfb\u7edf\u6027\u5730\u4e0a\u4e00\u95e8\u8f6f\u4ef6\u5de5\u7a0b\u7684\u8bfe\u7a0b\uff0c\u90a3\u6211\u63a8\u8350\u7684\u662f\u4f2f\u514b\u5229\u7684 UCB CS169: software engineering \u3002\u4f46\u9700\u8981\u63d0\u9192\u7684\u662f\uff0c\u548c\u5927\u591a\u5b66\u6821\uff08\u5305\u62ec\u8d35\u6821\uff09\u7684\u8f6f\u4ef6\u5de5\u7a0b\u8bfe\u7a0b\u4e0d\u540c\uff0c\u8fd9\u95e8\u8bfe\u4e0d\u4f1a\u6d89\u53ca\u4f20\u7edf\u7684 design and document \u6a21\u5f0f\uff0c\u5373\u5f3a\u8c03\u5404\u79cd\u7c7b\u56fe\u3001\u6d41\u7a0b\u56fe\u53ca\u6587\u6863\u8bbe\u8ba1\uff0c\u800c\u662f\u91c7\u7528\u8fd1\u4e9b\u5e74\u6d41\u884c\u8d77\u6765\u7684\u5c0f\u56e2\u961f\u5feb\u901f\u8fed\u4ee3 Agile Develepment \u5f00\u53d1\u6a21\u5f0f\u4ee5\u53ca\u5229\u7528\u4e91\u5e73\u53f0\u7684 Software as a service \u670d\u52a1\u6a21\u5f0f\u3002 \u4f53\u7cfb\u7ed3\u6784 \u5165\u95e8\u8bfe \u4ece\u5c0f\u6211\u5c31\u4e00\u76f4\u542c\u8bf4\uff0c\u8ba1\u7b97\u673a\u7684\u4e16\u754c\u662f\u7531 01 \u6784\u6210\u7684\uff0c\u6211\u4e0d\u7406\u89e3\u4f46\u5927\u53d7\u9707\u64bc\u3002\u5982\u679c\u4f60\u7684\u5185\u5fc3\u4e5f\u6000\u6709\u8fd9\u4efd\u597d\u5947\uff0c\u4e0d\u59a8\u82b1\u4e00\u5230\u4e24\u4e2a\u6708\u7684\u65f6\u95f4\u5b66\u4e60 Coursera: Nand2Tetris \u8fd9\u95e8\u65e0\u95e8\u69db\u7684\u8ba1\u7b97\u673a\u8bfe\u7a0b\u3002\u8fd9\u95e8\u9ebb\u96c0\u867d\u5c0f\u4e94\u810f\u4ff1\u5168\u7684\u8bfe\u7a0b\u4f1a\u4ece 01 \u5f00\u59cb\u8ba9\u4f60\u4eb2\u624b\u9020\u51fa\u4e00\u53f0\u8ba1\u7b97\u673a\uff0c\u5e76\u5728\u4e0a\u9762\u8fd0\u884c\u4fc4\u7f57\u65af\u65b9\u5757\u5c0f\u6e38\u620f\u3002\u4e00\u95e8\u8bfe\u91cc\u6db5\u76d6\u4e86\u7f16\u8bd1\u3001\u865a\u62df\u673a\u3001\u6c47\u7f16\u3001\u4f53\u7cfb\u7ed3\u6784\u3001\u6570\u5b57\u7535\u8def\u3001\u903b\u8f91\u95e8\u7b49\u7b49\u4ece\u4e0a\u81f3\u4e0b\u3001\u4ece\u8f6f\u81f3\u786c\u7684\u5404\u7c7b\u77e5\u8bc6\uff0c\u975e\u5e38\u5168\u9762\u3002\u96be\u5ea6\u4e0a\u4e5f\u662f\u901a\u8fc7\u7cbe\u5fc3\u7684\u8bbe\u8ba1\uff0c\u7565\u53bb\u4e86\u4f17\u591a\u73b0\u4ee3\u8ba1\u7b97\u673a\u590d\u6742\u7684\u7ec6\u8282\uff0c\u63d0\u53d6\u51fa\u4e86\u6700\u6838\u5fc3\u672c\u8d28\u7684\u4e1c\u897f\uff0c\u529b\u56fe\u8ba9\u6bcf\u4e2a\u4eba\u90fd\u80fd\u7406\u89e3\u3002\u5728\u4f4e\u5e74\u7ea7\uff0c\u5982\u679c\u5c31\u80fd\u4ece\u5b8f\u89c2\u4e0a\u5efa\u7acb\u5bf9\u6574\u4e2a\u8ba1\u7b97\u673a\u4f53\u7cfb\u7684\u9e1f\u77b0\u56fe\uff0c\u662f\u5927\u6709\u88e8\u76ca\u7684\u3002 \u4e13\u4e1a\u8bfe \u5f53\u7136\uff0c\u5982\u679c\u60f3\u6df1\u5165\u73b0\u4ee3\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\u7684\u590d\u6742\u7ec6\u8282\uff0c\u8fd8\u5f97\u4e0a\u4e00\u95e8\u5927\u5b66\u672c\u79d1\u96be\u5ea6\u7684\u8bfe\u7a0b UCB CS61C: Great Ideas in Computer Architecture \u3002UC Berkeley \u4f5c\u4e3a RISC-V \u67b6\u6784\u7684\u53d1\u6e90\u5730\uff0c\u5728\u4f53\u7cfb\u7ed3\u6784\u9886\u57df\u7b97\u5f97\u4e0a\u9996\u5c48\u4e00\u6307\u3002\u5176\u8bfe\u7a0b\u975e\u5e38\u6ce8\u91cd\u5b9e\u8df5\uff0c\u4f60\u4f1a\u5728 Project \u4e2d\u624b\u5199\u6c47\u7f16\u6784\u9020\u795e\u7ecf\u7f51\u7edc\uff0c\u4ece\u96f6\u5f00\u59cb\u642d\u5efa\u4e00\u4e2a CPU\uff0c\u8fd9\u4e9b\u5b9e\u8df5\u90fd\u4f1a\u8ba9\u4f60\u5bf9\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\u6709\u66f4\u4e3a\u6df1\u5165\u7684\u7406\u89e3\uff0c\u800c\u4e0d\u662f\u4ec5\u505c\u7559\u4e8e\u201c\u53d6\u6307\u8bd1\u7801\u6267\u884c\u8bbf\u5b58\u5199\u56de\u201d\u7684\u5355\u8c03\u80cc\u8bf5\u91cc\u3002 \u7cfb\u7edf\u5165\u95e8 \u8ba1\u7b97\u673a\u7cfb\u7edf\u662f\u4e00\u4e2a\u5e9e\u6742\u800c\u6df1\u523b\u7684\u4e3b\u9898\uff0c\u5728\u6df1\u5165\u5b66\u4e60\u67d0\u4e2a\u7ec6\u5206\u9886\u57df\u4e4b\u524d\uff0c\u5bf9\u5404\u4e2a\u9886\u57df\u6709\u4e00\u4e2a\u5b8f\u89c2\u6982\u5ff5\u6027\u7684\u7406\u89e3\uff0c\u5bf9\u4e00\u4e9b\u901a\u7528\u6027\u7684\u8bbe\u8ba1\u539f\u5219\u6709\u6240\u77e5\u6653\uff0c\u4f1a\u8ba9\u4f60\u5728\u4e4b\u540e\u7684\u6df1\u5165\u5b66\u4e60\u4e2d\u4e0d\u65ad\u5f3a\u5316\u4e00\u4e9b\u6700\u4e3a\u6838\u5fc3\u4e43\u81f3\u54f2\u5b66\u7684\u6982\u5ff5\uff0c\u800c\u4e0d\u4f1a\u684e\u688f\u4e8e\u590d\u6742\u7684\u5185\u90e8\u7ec6\u8282\u548c\u5404\u79cd trick\u3002\u56e0\u4e3a\u5728\u6211\u770b\u6765\uff0c\u5b66\u4e60\u7cfb\u7edf\u6700\u5173\u952e\u7684\u8fd8\u662f\u60f3\u8ba9\u4f60\u9886\u609f\u5230\u8fd9\u4e9b\u6700\u6838\u5fc3\u7684\u4e1c\u897f\uff0c\u4ece\u800c\u80fd\u591f\u8bbe\u8ba1\u548c\u5b9e\u73b0\u51fa\u5c5e\u4e8e\u81ea\u5df1\u7684\u7cfb\u7edf\u3002 MIT6.033: System Engineering \u662f MIT \u7684\u7cfb\u7edf\u5165\u95e8\u8bfe\uff0c\u4e3b\u9898\u6d89\u53ca\u4e86\u64cd\u4f5c\u7cfb\u7edf\u3001\u7f51\u7edc\u3001\u5206\u5e03\u5f0f\u548c\u7cfb\u7edf\u5b89\u5168\uff0c\u9664\u4e86\u77e5\u8bc6\u70b9\u7684\u4f20\u6388\u5916\uff0c\u8fd9\u95e8\u8bfe\u8fd8\u4f1a\u8bb2\u6388\u4e00\u4e9b\u5199\u4f5c\u548c\u8868\u8fbe\u4e0a\u7684\u6280\u5de7\uff0c\u8ba9\u4f60\u5b66\u4f1a\u5982\u4f55\u8bbe\u8ba1\u5e76\u5411\u522b\u4eba\u4ecb\u7ecd\u548c\u5206\u6790\u81ea\u5df1\u7684\u7cfb\u7edf\u3002\u8fd9\u672c\u4e66\u914d\u5957\u7684\u6559\u6750 Principles of Computer System Design: An Introduction \u4e5f\u5199\u5f97\u975e\u5e38\u597d\uff0c\u63a8\u8350\u5927\u5bb6\u9605\u8bfb\u3002 CMU 15-213: Introduction to Computer System \u662f CMU 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OCaml","title":"OCaml"},{"location":"CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_20","text":"","title":"\u7535\u5b50\u57fa\u7840"},{"location":"CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_21","text":"\u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u4e86\u89e3\u4e00\u4e9b\u57fa\u7840\u7684\u7535\u8def\u77e5\u8bc6\uff0c\u611f\u53d7\u4ece\u4f20\u611f\u5668\u6536\u96c6\u6570\u636e\u5230\u6570\u636e\u5206\u6790\u518d\u5230\u7b97\u6cd5\u9884\u6d4b\u6574\u6761\u6d41\u6c34\u7ebf\uff0c\u5bf9\u4e8e\u540e\u7eed\u77e5\u8bc6\u7684\u5b66\u4e60\u4ee5\u53ca\u8ba1\u7b97\u601d\u7ef4\u7684\u57f9\u517b\u8fd8\u662f\u5f88\u6709\u5e2e\u52a9\u7684\u3002 EE16A&B: Designing Information Devices and Systems I&II \u662f\u4f2f\u514b\u5229 EE \u5b66\u751f\u7684\u5927\u4e00\u5165\u95e8\u8bfe\uff0c\u5176\u4e2d EE16A \u6ce8\u91cd\u901a\u8fc7\u7535\u8def\u4ece\u5b9e\u9645\u73af\u5883\u4e2d\u6536\u96c6\u548c\u5206\u6790\u6570\u636e\uff0c\u800c EE16B 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construction.","title":"\u57f9\u517b\u65b9\u6848Pro"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/","text":"\u597d\u4e66\u63a8\u8350 \u7531\u4e8e\u7248\u6743\u539f\u56e0\uff0c\u4e0b\u9762\u5217\u4e3e\u7684\u56fe\u4e66\u4e2d\u9664\u4e86\u5f00\u6e90\u8d44\u6e90\u63d0\u4f9b\u4e86\u94fe\u63a5\uff0c\u5176\u4ed6\u7684\u8d44\u6e90\u8bf7\u5927\u5bb6\u81ea\u884c\u901a\u8fc7 libgen \u6216 z-lib \u67e5\u627e\u3002 \u8d44\u6e90\u6c47\u603b Free Programming Books : \u5f00\u6e90\u7f16\u7a0b\u4e66\u7c4d\u8d44\u6e90\u6c47\u603b CS Textbook Recommendations : \u8ba1\u7b97\u673a\u79d1\u5b66\u65b9\u5411\u63a8\u8350\u6559\u6750\u5217\u8868 C Book Guide and List : C\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 C++ Book Guide and List : C++\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 Python Book Guide and List : Python\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 \u7cfb\u7edf\u5165\u95e8 Computer Systems: A Programmer's Perspective [ \u8c46\u74e3 ] Principles of Computer System Design: An Introduction [ \u8c46\u74e3 ] \u64cd\u4f5c\u7cfb\u7edf \u73b0\u4ee3\u64cd\u4f5c\u7cfb\u7edf: \u539f\u7406\u4e0e\u5b9e\u73b0 [ \u8c46\u74e3 ] Operating Systems: Three Easy Pieces [ \u8c46\u74e3 ] Modern Operating Systems [ \u8c46\u74e3 ] Operating Systems: Principles and Practice [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u7f51\u7edc Computer Networks: A Systems Approach [ \u8c46\u74e3 ] Computer Networking: A Top-Down Approach [ \u8c46\u74e3 ] \u5206\u5e03\u5f0f\u7cfb\u7edf Patterns of Distributed System (Blog) Distributed Systems for Fun and Profit (Blog) Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems [ \u8c46\u74e3 ] \u6570\u636e\u5e93\u7cfb\u7edf Architecture of a Database System [ \u8c46\u74e3 ] Readings in Database Systems [ \u8c46\u74e3 ] Database System Concepts [ \u8c46\u74e3 ] \u7f16\u8bd1\u539f\u7406 Engineering a Compiler [ \u8c46\u74e3 ] Compilers: Principles, Techniques, and Tools [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u7f16\u7a0b\u8bed\u8a00 \u8ba1\u7b97\u673a\u7a0b\u5e8f\u7684\u6784\u9020\u548c\u89e3\u91ca [ \u8c46\u74e3 ] Essentials of Programming Languages [ \u8c46\u74e3 ] Practical Foundations for Programming Languages [ \u8c46\u74e3 ] Software Foundations [ \u8c46\u74e3 ] [ \u5317\u5927\u76f8\u5173\u8bfe\u7a0b ] Types and Programming Languages [ \u8c46\u74e3 ] [ \u5317\u5927\u76f8\u5173\u8bfe\u7a0b ] \u4f53\u7cfb\u7ed3\u6784 \u8d85\u6807\u91cf\u5904\u7406\u5668\u8bbe\u8ba1: Superscalar RISC Processor Design [ \u8c46\u74e3 ] Computer Organization and Design RISC-V Edition [ \u8c46\u74e3 ] Computer Organization and Design: The Hardware/Software Interface [ \u8c46\u74e3 ] Computer Architecture: A Quantitative Approach [ \u8c46\u74e3 ] \u7406\u8bba\u8ba1\u7b97\u673a\u79d1\u5b66 Introduction to the Theory of Computation [ \u8c46\u74e3 ] \u5bc6\u7801\u5b66 Cryptography Engineering: Design Principles and Practical Applications [ \u8c46\u74e3 ] Introduction to Modern Cryptography [ \u8c46\u74e3 ] \u9006\u5411\u5de5\u7a0b \u9006\u5411\u5de5\u7a0b\u6838\u5fc3\u539f\u7406 [ \u8c46\u74e3 ] \u52a0\u5bc6\u4e0e\u89e3\u5bc6 [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u56fe\u5f62\u5b66 Monte Carlo theory, methods and examples Advanced Global Illumination [ \u8c46\u74e3 ] Fundamentals of Computer Graphics [ \u8c46\u74e3 ] Fluid Simulation for Computer Graphics [ \u8c46\u74e3 ] Physically Based Rendering: From Theory To Implementation [ \u8c46\u74e3 ] Real-Time Rendering [ \u8c46\u74e3 ] \u6e38\u620f\u5f15\u64ce \u6e38\u620f\u7f16\u7a0b\u6a21\u5f0f: Game Programming Patterns [ \u8c46\u74e3 ] \u5b9e\u65f6\u78b0\u649e\u68c0\u6d4b\u7b97\u6cd5\u6280\u672f [ \u8c46\u74e3 ] Game AI Pro Series [ \u8c46\u74e3 ] Artificial Intelligence for Games [ \u8c46\u74e3 ] Game Engine Architecture [ \u8c46\u74e3 ] Game Programming Gems Series [ \u8c46\u74e3 ] \u8f6f\u4ef6\u5de5\u7a0b Software Engineering at Google [ \u8c46\u74e3 ] \u8bbe\u8ba1\u6a21\u5f0f \u8bbe\u8ba1\u6a21\u5f0f: \u53ef\u590d\u7528\u9762\u5411\u5bf9\u8c61\u8f6f\u4ef6\u7684\u57fa\u7840 [ \u8c46\u74e3 ] \u5927\u8bdd\u8bbe\u8ba1\u6a21\u5f0f [ \u8c46\u74e3 ] Head First \u8bbe\u8ba1\u6a21\u5f0f [ \u8c46\u74e3 ] \u6df1\u5ea6\u5b66\u4e60 \u52a8\u624b\u5b66\u6df1\u5ea6\u5b66\u4e60 [ \u8c46\u74e3 ] \u795e\u7ecf\u7f51\u7edc\u4e0e\u6df1\u5ea6\u5b66\u4e60 [ \u8c46\u74e3 ] \u6df1\u5ea6\u5b66\u4e60\u5165\u95e8 [ \u8c46\u74e3 ] \u7b80\u5355\u7c97\u66b4 TensorFlow 2 (Tutorial) Speech and Language Processing [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u89c6\u89c9 Multiple View Geometry in Computer Vision [ \u8c46\u74e3 ] \u673a\u5668\u4eba Probabilistic Robotics [ \u8c46\u74e3 ] \u9762\u8bd5 \u5251\u6307 Offer\uff1a\u540d\u4f01\u9762\u8bd5\u5b98\u7cbe\u8bb2\u5178\u578b\u7f16\u7a0b\u9898 [ \u8c46\u74e3 ] Cracking The Coding Interview [ \u8c46\u74e3 ]","title":"\u597d\u4e66\u63a8\u8350"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_1","text":"\u7531\u4e8e\u7248\u6743\u539f\u56e0\uff0c\u4e0b\u9762\u5217\u4e3e\u7684\u56fe\u4e66\u4e2d\u9664\u4e86\u5f00\u6e90\u8d44\u6e90\u63d0\u4f9b\u4e86\u94fe\u63a5\uff0c\u5176\u4ed6\u7684\u8d44\u6e90\u8bf7\u5927\u5bb6\u81ea\u884c\u901a\u8fc7 libgen \u6216 z-lib \u67e5\u627e\u3002","title":"\u597d\u4e66\u63a8\u8350"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_2","text":"Free Programming Books : \u5f00\u6e90\u7f16\u7a0b\u4e66\u7c4d\u8d44\u6e90\u6c47\u603b CS Textbook Recommendations : \u8ba1\u7b97\u673a\u79d1\u5b66\u65b9\u5411\u63a8\u8350\u6559\u6750\u5217\u8868 C Book Guide and List : C\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 C++ Book Guide and List : C++\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 Python Book Guide and List : 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Distributed System (Blog) Distributed Systems for Fun and Profit (Blog) Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems [ \u8c46\u74e3 ]","title":"\u5206\u5e03\u5f0f\u7cfb\u7edf"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_7","text":"Architecture of a Database System [ \u8c46\u74e3 ] Readings in Database Systems [ \u8c46\u74e3 ] Database System Concepts [ \u8c46\u74e3 ]","title":"\u6570\u636e\u5e93\u7cfb\u7edf"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_8","text":"Engineering a Compiler [ \u8c46\u74e3 ] Compilers: Principles, Techniques, and Tools [ \u8c46\u74e3 ]","title":"\u7f16\u8bd1\u539f\u7406"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_9","text":"\u8ba1\u7b97\u673a\u7a0b\u5e8f\u7684\u6784\u9020\u548c\u89e3\u91ca [ \u8c46\u74e3 ] Essentials of Programming Languages [ \u8c46\u74e3 ] Practical Foundations for Programming Languages [ \u8c46\u74e3 ] Software Foundations [ \u8c46\u74e3 ] [ 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Bryant \u6559\u6388\u6267\u7b14\uff0c\u4e5f\u5373\u6240\u8c13\u7684 CSAPP\u3002\u8fd9\u4e5f\u662f\u6211\u7b2c\u4e00\u672c\u8ba4\u8ba4\u771f\u771f\u4e00\u9875\u4e00\u9875\u8bfb\u8fc7\u53bb\u7684\u8ba1\u7b97\u673a\u6559\u6750\uff0c\u867d\u7136\u5f88\u96be\u5543\uff0c\u4f46\u7740\u5b9e\u6536\u83b7\u826f\u591a\u3002 \u5317\u5927\u8d2d\u4e70\u4e86\u8fd9\u95e8\u8bfe\u7684\u7248\u6743\u5e76\u5f00\u8bbe\u4e86 Introduction to Computer System \u8fd9\u95e8\u8bfe\uff0c\u4f46\u5176\u5b9e CSAPP \u6240\u6709\u7684\u8bfe\u7a0b\u8d44\u6e90\u548c\u5b9e\u9a8c\u4ee3\u7801\u90fd\u80fd\u5728\u5b83\u7684\u5b98\u65b9\u4e3b\u9875\u4e0a\u8bbf\u95ee\u5230\uff08\u5177\u4f53\u53c2\u89c1\u4e0b\u65b9\u94fe\u63a5\uff09\u3002 \u8fd9\u95e8\u8bfe\u7531\u4e8e\u8fc7\u4e8e\u51fa\u540d\uff0c\u5168\u4e16\u754c\u7684\u7801\u519c\u4e89\u76f8\u5b66\u4e60\uff0c\u5bfc\u81f4\u5176 Project 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\u5199\u4f5c\u4e1a\u4e5f\u662f\u4e00\u4e2a\u4e0d\u9519\u7684\u9009\u62e9\u3002","title":"LaTeX"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/LaTeX/#latex","text":"","title":"LaTeX"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/LaTeX/#latex_1","text":"\u5982\u679c\u4f60\u9700\u8981\u5199\u8bba\u6587\uff0c\u90a3\u4e48\u8bf7\u76f4\u63a5\u8df3\u5230\u4e0b\u4e00\u8282\uff0c\u56e0\u4e3a\u4f60\u4e0d\u5b66\u4e5f\u5f97\u5b66\u3002 LaTeX \u662f\u4e00\u79cd\u57fa\u4e8e TeX \u7684\u6392\u7248\u7cfb\u7edf\uff0c\u7531\u56fe\u7075\u5956\u5f97\u4e3b Lamport \u5f00\u53d1\uff0c\u800c Tex \u5219\u662f\u7531 Knuth \u6700\u521d\u5f00\u53d1\uff0c\u8fd9\u4e24\u4f4d\u90fd\u662f\u8ba1\u7b97\u673a\u754c\u7684\u5de8\u64d8\u3002\u5f53\u7136\u5f00\u53d1\u8005\u5f3a\u5e76\u4e0d\u662f\u6211\u4eec\u5b66\u4e60 LaTeX \u7684\u7406\u7531\uff0cLaTeX \u548c\u5e38\u89c1\u7684\u6240\u89c1\u5373\u6240\u5f97\u7684 Word 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\u73af\u5883\u51fa\u73b0\u4e86\u95ee\u9898\uff0c\u53ef\u4ee5\u8003\u8651\u4f7f\u7528 Overleaf \u8fd9\u4e2a\u5728\u7ebf LaTeX \u7f16\u8f91\u7f51\u7ad9\u3002\u7ad9\u5185\u4e0d\u4ec5\u6709\u5404\u79cd\u5404\u6837\u7684 LaTeX \u6a21\u7248\u4f9b\u4f60\u9009\u62e9\uff0c\u8fd8\u514d\u53bb\u4e86\u73af\u5883\u914d\u7f6e\u7684\u96be\u9898\u3002 \u9605\u8bfb\u4e0b\u9762\u4e09\u7bc7 Tutorial: Part-1 , Part-2 , Part-3 \u3002 \u5b66\u4e60 LaTeX \u6700\u597d\u7684\u65b9\u5f0f\u5f53\u7136\u662f\u5199\u8bba\u6587\uff0c\u4e0d\u8fc7\u4ece\u4e00\u95e8\u6570\u5b66\u8bfe\u5165\u624b\u7528 LaTeX \u5199\u4f5c\u4e1a\u4e5f\u662f\u4e00\u4e2a\u4e0d\u9519\u7684\u9009\u62e9\u3002","title":"\u5982\u4f55\u5b66\u4e60 LaTeX"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/","text":"Vim \u4e3a\u4ec0\u4e48\u5b66\u4e60 Vim \u5728\u6211\u770b\u6765 Vim \u7f16\u8f91\u5668\u6709\u5982\u4e0b\u7684\u597d\u5904\uff1a \u8ba9\u4f60\u7684\u6574\u4e2a\u5f00\u53d1\u8fc7\u7a0b\u624b\u6307\u4e0d\u9700\u8981\u79bb\u5f00\u952e\u76d8\uff0c\u800c\u4e14\u5149\u6807\u7684\u79fb\u52a8\u4e0d\u9700\u8981\u65b9\u5411\u952e\u4f7f\u5f97\u4f60\u7684\u624b\u6307\u4e00\u76f4\u5904\u5728\u6253\u5b57\u7684\u6700\u4f73\u4f4d\u7f6e\u3002 \u65b9\u4fbf\u7684\u6587\u4ef6\u5207\u6362\u4ee5\u53ca\u9762\u677f\u63a7\u5236\u53ef\u4ee5\u8ba9\u4f60\u540c\u65f6\u5f00\u53d1\u591a\u4efd\u6587\u4ef6\u751a\u81f3\u540c\u4e00\u4e2a\u6587\u4ef6\u7684\u4e0d\u540c\u4f4d\u7f6e\u3002 Vim \u7684\u5b8f\u64cd\u4f5c\u53ef\u4ee5\u6279\u91cf\u5316\u5904\u7406\u91cd\u590d\u64cd\u4f5c\uff08\u4f8b\u5982\u591a\u884c tab\uff0c\u6279\u91cf\u52a0\u53cc\u5f15\u53f7\u7b49\u7b49\uff09 Vim \u662f\u5f88\u591a\u670d\u52a1\u5668\u81ea\u5e26\u7684\u547d\u4ee4\u884c\u7f16\u8f91\u5668\uff0c\u5f53\u4f60\u901a\u8fc7 ssh \u8fde\u63a5\u8fdc\u7a0b\u670d\u52a1\u5668\u4e4b\u540e\uff0c\u7531\u4e8e\u6ca1\u6709\u56fe\u5f62\u754c\u9762\uff0c\u53ea\u80fd\u5728\u547d\u4ee4\u884c\u91cc\u8fdb\u884c\u5f00\u53d1\uff08\u5f53\u7136\u73b0\u5728\u5f88\u591a IDE \u5982 VS Code \u63d0\u4f9b\u4e86 ssh \u63d2\u4ef6\u53ef\u4ee5\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff09\u3002 \u5f02\u5e38\u4e30\u5bcc\u7684\u63d2\u4ef6\u751f\u6001\uff0c\u8ba9\u4f60\u62e5\u6709\u4e16\u754c\u4e0a\u6700\u82b1\u91cc\u80e1\u54e8\u7684\u547d\u4ee4\u884c\u7f16\u8f91\u5668\u3002 \u5982\u4f55\u5b66\u4e60 Vim \u4e0d\u5e78\u7684\u662f Vim \u7684\u5b66\u4e60\u66f2\u7ebf\u786e\u5b9e\u76f8\u5f53\u9661\u5ced\uff0c\u6211\u82b1\u4e86\u597d\u51e0\u4e2a\u661f\u671f\u624d\u6162\u6162\u9002\u5e94\u4e86\u7528 Vim \u8fdb\u884c\u5f00\u53d1\u7684\u8fc7\u7a0b\u3002\u6700\u5f00\u59cb\u4f60\u4f1a\u89c9\u5f97\u975e\u5e38\u4e0d\u9002\u5e94\uff0c\u4f46\u4e00\u65e6\u71ac\u8fc7\u4e86\u521d\u59cb\u9636\u6bb5\uff0c\u76f8\u4fe1\u6211\uff0c\u4f60\u4f1a\u7231\u4e0a Vim\u3002 Vim \u7684\u5b66\u4e60\u8d44\u6599\u6d69\u5982\u70df\u6d77\uff0c\u4f46\u638c\u63e1\u5b83\u6700\u597d\u7684\u65b9\u5f0f\u8fd8\u662f\u5c06\u5b83\u7528\u5728\u65e5\u5e38\u7684\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u800c\u4e0d\u662f\u4e00\u4e0a\u6765\u5c31\u53bb\u5b66\u5404\u79cd\u82b1\u91cc\u80e1\u54e8\u7684\u9ad8\u7ea7 Vim \u6280\u5de7\u3002\u4e2a\u4eba\u63a8\u8350\u7684\u5b66\u4e60\u8def\u7ebf\u5982\u4e0b\uff1a \u5148\u9605\u8bfb \u8fd9\u7bc7 tutorial \uff0c\u638c\u63e1\u57fa\u672c\u7684 Vim \u6982\u5ff5\u548c\u4f7f\u7528\u65b9\u5f0f\u3002 \u7528 Vim \u81ea\u5e26\u7684 vimtutor \u8fdb\u884c\u7ec3\u4e60\uff0c\u5b89\u88c5\u5b8c Vim \u4e4b\u540e\u76f4\u63a5\u5728\u547d\u4ee4\u884c\u91cc\u8f93\u5165 vimtutor \u5373\u53ef\u8fdb\u5165\u7ec3\u4e60\u7a0b\u5e8f\u3002 \u6700\u540e\u5c31\u662f\u5f3a\u8feb\u81ea\u5df1\u4f7f\u7528 Vim \u8fdb\u884c\u5f00\u53d1\uff0cIDE \u91cc\u53ef\u4ee5\u5b89\u88c5 Vim \u63d2\u4ef6\u3002 \u7b49\u4f60\u5b8c\u5168\u9002\u5e94 Vim \u4e4b\u540e\u65b0\u7684\u4e16\u754c\u4fbf\u5411\u4f60\u655e\u5f00\u4e86\u5927\u95e8\uff0c\u4f60\u53ef\u4ee5\u6309\u9700\u914d\u7f6e\u81ea\u5df1\u7684 Vim\uff08\u4fee\u6539 .vimrc \u6587\u4ef6\uff09\uff0c\u7f51\u4e0a\u6709\u6570\u4e0d\u80dc\u6570\u7684\u8d44\u6e90\u53ef\u4ee5\u501f\u9274\u3002 \u63a8\u8350\u53c2\u8003\u8d44\u6599 Neil, Drew. Practical Vim: Edit Text at the Speed of Thought. N.p., Pragmatic Bookshelf, 2015. Neil, Drew. Modern Vim: Craft Your Development Environment with Vim 8 and Neovim. United States, Pragmatic Bookshelf.","title":"Vim"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#vim","text":"","title":"Vim"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#vim_1","text":"\u5728\u6211\u770b\u6765 Vim \u7f16\u8f91\u5668\u6709\u5982\u4e0b\u7684\u597d\u5904\uff1a \u8ba9\u4f60\u7684\u6574\u4e2a\u5f00\u53d1\u8fc7\u7a0b\u624b\u6307\u4e0d\u9700\u8981\u79bb\u5f00\u952e\u76d8\uff0c\u800c\u4e14\u5149\u6807\u7684\u79fb\u52a8\u4e0d\u9700\u8981\u65b9\u5411\u952e\u4f7f\u5f97\u4f60\u7684\u624b\u6307\u4e00\u76f4\u5904\u5728\u6253\u5b57\u7684\u6700\u4f73\u4f4d\u7f6e\u3002 \u65b9\u4fbf\u7684\u6587\u4ef6\u5207\u6362\u4ee5\u53ca\u9762\u677f\u63a7\u5236\u53ef\u4ee5\u8ba9\u4f60\u540c\u65f6\u5f00\u53d1\u591a\u4efd\u6587\u4ef6\u751a\u81f3\u540c\u4e00\u4e2a\u6587\u4ef6\u7684\u4e0d\u540c\u4f4d\u7f6e\u3002 Vim \u7684\u5b8f\u64cd\u4f5c\u53ef\u4ee5\u6279\u91cf\u5316\u5904\u7406\u91cd\u590d\u64cd\u4f5c\uff08\u4f8b\u5982\u591a\u884c tab\uff0c\u6279\u91cf\u52a0\u53cc\u5f15\u53f7\u7b49\u7b49\uff09 Vim \u662f\u5f88\u591a\u670d\u52a1\u5668\u81ea\u5e26\u7684\u547d\u4ee4\u884c\u7f16\u8f91\u5668\uff0c\u5f53\u4f60\u901a\u8fc7 ssh \u8fde\u63a5\u8fdc\u7a0b\u670d\u52a1\u5668\u4e4b\u540e\uff0c\u7531\u4e8e\u6ca1\u6709\u56fe\u5f62\u754c\u9762\uff0c\u53ea\u80fd\u5728\u547d\u4ee4\u884c\u91cc\u8fdb\u884c\u5f00\u53d1\uff08\u5f53\u7136\u73b0\u5728\u5f88\u591a IDE \u5982 VS Code \u63d0\u4f9b\u4e86 ssh \u63d2\u4ef6\u53ef\u4ee5\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff09\u3002 \u5f02\u5e38\u4e30\u5bcc\u7684\u63d2\u4ef6\u751f\u6001\uff0c\u8ba9\u4f60\u62e5\u6709\u4e16\u754c\u4e0a\u6700\u82b1\u91cc\u80e1\u54e8\u7684\u547d\u4ee4\u884c\u7f16\u8f91\u5668\u3002","title":"\u4e3a\u4ec0\u4e48\u5b66\u4e60 Vim"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#vim_2","text":"\u4e0d\u5e78\u7684\u662f Vim \u7684\u5b66\u4e60\u66f2\u7ebf\u786e\u5b9e\u76f8\u5f53\u9661\u5ced\uff0c\u6211\u82b1\u4e86\u597d\u51e0\u4e2a\u661f\u671f\u624d\u6162\u6162\u9002\u5e94\u4e86\u7528 Vim \u8fdb\u884c\u5f00\u53d1\u7684\u8fc7\u7a0b\u3002\u6700\u5f00\u59cb\u4f60\u4f1a\u89c9\u5f97\u975e\u5e38\u4e0d\u9002\u5e94\uff0c\u4f46\u4e00\u65e6\u71ac\u8fc7\u4e86\u521d\u59cb\u9636\u6bb5\uff0c\u76f8\u4fe1\u6211\uff0c\u4f60\u4f1a\u7231\u4e0a Vim\u3002 Vim \u7684\u5b66\u4e60\u8d44\u6599\u6d69\u5982\u70df\u6d77\uff0c\u4f46\u638c\u63e1\u5b83\u6700\u597d\u7684\u65b9\u5f0f\u8fd8\u662f\u5c06\u5b83\u7528\u5728\u65e5\u5e38\u7684\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u800c\u4e0d\u662f\u4e00\u4e0a\u6765\u5c31\u53bb\u5b66\u5404\u79cd\u82b1\u91cc\u80e1\u54e8\u7684\u9ad8\u7ea7 Vim \u6280\u5de7\u3002\u4e2a\u4eba\u63a8\u8350\u7684\u5b66\u4e60\u8def\u7ebf\u5982\u4e0b\uff1a \u5148\u9605\u8bfb \u8fd9\u7bc7 tutorial \uff0c\u638c\u63e1\u57fa\u672c\u7684 Vim \u6982\u5ff5\u548c\u4f7f\u7528\u65b9\u5f0f\u3002 \u7528 Vim \u81ea\u5e26\u7684 vimtutor \u8fdb\u884c\u7ec3\u4e60\uff0c\u5b89\u88c5\u5b8c Vim \u4e4b\u540e\u76f4\u63a5\u5728\u547d\u4ee4\u884c\u91cc\u8f93\u5165 vimtutor \u5373\u53ef\u8fdb\u5165\u7ec3\u4e60\u7a0b\u5e8f\u3002 \u6700\u540e\u5c31\u662f\u5f3a\u8feb\u81ea\u5df1\u4f7f\u7528 Vim \u8fdb\u884c\u5f00\u53d1\uff0cIDE \u91cc\u53ef\u4ee5\u5b89\u88c5 Vim \u63d2\u4ef6\u3002 \u7b49\u4f60\u5b8c\u5168\u9002\u5e94 Vim \u4e4b\u540e\u65b0\u7684\u4e16\u754c\u4fbf\u5411\u4f60\u655e\u5f00\u4e86\u5927\u95e8\uff0c\u4f60\u53ef\u4ee5\u6309\u9700\u914d\u7f6e\u81ea\u5df1\u7684 Vim\uff08\u4fee\u6539 .vimrc \u6587\u4ef6\uff09\uff0c\u7f51\u4e0a\u6709\u6570\u4e0d\u80dc\u6570\u7684\u8d44\u6e90\u53ef\u4ee5\u501f\u9274\u3002","title":"\u5982\u4f55\u5b66\u4e60 Vim"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#_1","text":"Neil, Drew. Practical Vim: Edit Text at the Speed of Thought. N.p., Pragmatic Bookshelf, 2015. Neil, Drew. Modern Vim: Craft Your Development Environment with Vim 8 and Neovim. United States, Pragmatic Bookshelf.","title":"\u63a8\u8350\u53c2\u8003\u8d44\u6599"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/thesis/","text":"\u6bd5\u4e1a\u8bba\u6587 \u4e3a\u4ec0\u4e48\u5199\u8fd9\u4efd\u6559\u7a0b 2022\u5e74\uff0c\u6211\u672c\u79d1\u6bd5\u4e1a\u4e86\u3002\u5728\u5f00\u59cb\u52a8\u624b\u5199\u6bd5\u4e1a\u8bba\u6587\u7684\u65f6\u5019\uff0c\u6211\u5c34\u5c2c\u5730\u53d1\u73b0\uff0c\u6211\u5bf9 Word \u7684\u638c\u63e1\u7a0b\u5ea6\u4ec5\u9650\u4e8e\u8c03\u8282\u5b57\u4f53\u3001\u4fdd\u5b58\u5bfc\u51fa\u8fd9\u4e9b\u50bb\u74dc\u529f\u80fd\u3002\u66fe\u60f3\u8f6c\u6218 Latex\uff0c\u4f46\u8bba\u6587\u7684\u6bb5\u843d\u683c\u5f0f\u8981\u6c42\u8c03\u6574\u8d77\u6765\u8fd8\u662f\u7528 Word 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\uff0c\u5e76\u6309\u7167\u5176\u8981\u6c42\u5236\u4f5c\u4e86 \u4e00\u4efd\u6a21\u7248 \uff0c\u5927\u5bb6\u9700\u8981\u7684\u8bdd\u81ea\u53d6\uff0c\u672c\u4eba\u4e0d\u627f\u62c5\u65e0\u6cd5\u6bd5\u4e1a\u7b49\u4efb\u4f55\u8d23\u4efb\u3002 \u5b66\u4e60 Word \u6392\u7248\uff1a\u5230\u8fbe\u8fd9\u4e00\u6b65\u7684\u7ae5\u978b\u5206\u4e3a\u4e24\u7c7b\uff0c\u4e00\u662f\u5df2\u7ecf\u62e5\u6709\u4e86\u5b66\u9662\u63d0\u4f9b\u7684\u6807\u51c6\u6a21\u7248\uff0c\u4e8c\u662f\u53ea\u6709\u4e00\u4efd\u865a\u65e0\u7f25\u7f08\u7684\u683c\u5f0f\u8981\u6c42\u3002\u90a3\u73b0\u5728\u5f53\u52a1\u4e4b\u6025\u5c31\u662f\u5b66\u4e60\u57fa\u7840\u7684 Word \u6392\u7248\u6280\u672f\uff0c\u5bf9\u4e8e\u524d\u8005\u53ef\u4ee5\u5b66\u4f1a\u4f7f\u7528\u6a21\u7248\uff0c\u5bf9\u4e8e\u540e\u8005\u5219\u53ef\u4ee5\u5b66\u4f1a\u5236\u4f5c\u6a21\u7248\u3002\u6b64\u65f6\u5207\u8bb0\u4e0d\u8981\u96c4\u5fc3\u52c3\u52c3\u5730\u9009\u62e9\u4e00\u4e2a\u5341\u51e0\u4e2a\u5c0f\u65f6\u7684 Word \u6559\u5b66\u89c6\u9891\u5f00\u59cb\u5934\u60ac\u6881\u9525\u523a\u80a1\uff0c\u56e0\u4e3a\u751f\u4ea7\u4e00\u4efd\u5e94\u4ed8\u6bd5\u4e1a\u7684\u5b66\u672f\u5783\u573e\u53ea\u8981\u5b66\u534a\u5c0f\u65f6\u80fd\u4e0a\u624b\u5c31\u591f\u4e86\u3002\u6211\u5f53\u65f6\u770b\u7684 \u4e00\u4e2a B \u7ad9\u7684\u6559\u5b66\u89c6\u9891 \uff0c\u77ed\u5c0f\u7cbe\u608d\u975e\u5e38\u5b9e\u7528\uff0c\u5168\u957f\u534a\u5c0f\u65f6\u6781\u901f\u5165\u95e8\u3002 \u751f\u4ea7\u5b66\u672f\u5783\u573e\uff1a\u6700\u5bb9\u6613\u7684\u4e00\u6b65\uff0c\u5927\u5bb6\u516b\u4ed9\u8fc7\u6d77\uff0c\u5404\u663e\u795e\u901a\u5427\uff0c\u795d\u5927\u5bb6\u6bd5\u4e1a\u987a\u5229\uff5e\uff5e","title":"\u5982\u4f55\u7528 Word \u5199\u6bd5\u4e1a\u8bba\u6587"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/","text":"\u5b9e\u7528\u5de5\u5177\u7bb1 \u4e0b\u8f7d\u5de5\u5177 Libgen : PDF\u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9\u3002 z-epub : ePub\u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9\u3002 bitdownloader : \u6cb9\u7ba1\u89c6\u9891\u4e0b\u8f7d\u5668\u3002 zlibrary : \u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9(\u53ef\u80fd\u9700\u8981\u7ffb\u5899)\u3002 \u8bbe\u8ba1\u5de5\u5177 excalidraw : \u4e00\u6b3e\u624b\u7ed8\u98ce\u683c\u7684\u7ed8\u56fe\u5de5\u5177\uff0c\u975e\u5e38\u9002\u5408\u7ed8\u5236\u8bfe\u7a0b\u62a5\u544a\u6216\u8005PPT\u5185\u7684\u793a\u610f\u56fe\u3002 origamiway : \u624b\u628a\u624b\u6559\u4f60\u600e\u4e48\u6298\u7eb8\u3002 thingiverse : \u56ca\u62ec\u5404\u7c7b 2D/3D \u8bbe\u8ba1\u8d44\u6e90\uff0c\u5176 STL \u6587\u4ef6\u4e0b\u8f7d\u53ef\u76f4\u63a5 3D \u6253\u5370\u3002 iconfont : \u56fd\u5185\u6700\u5927\u7684\u56fe\u6807\u548c\u63d2\u753b\u8d44\u6e90\u5e93\uff0c\u53ef\u7528\u4e8e\u5f00\u53d1\u6216\u7ed8\u5236\u7cfb\u7edf\u67b6\u6784\u56fe\u3002 turbosquid : \u53ef\u4ee5\u8d2d\u4e70\u5404\u5f0f\u5404\u6837\u7684\u6a21\u578b\u3002 \u7f16\u7a0b\u76f8\u5173 sqlfiddle : \u4e00\u4e2a\u7b80\u6613\u7684\u5728\u7ebf SQL Playground\u3002 godbolt : \u975e\u5e38\u65b9\u4fbf\u7684\u7f16\u8bd1\u5668\u63a2\u7d22\u5de5\u5177\u3002\u4f60\u53ef\u4ee5\u5199\u4e00\u6bb5 C/C++ \u4ee3\u7801\uff0c\u9009\u62e9\u4e00\u6b3e\u7f16\u8bd1\u5668\uff0c\u7136\u540e\u4fbf\u53ef\u4ee5\u89c2\u5bdf\u751f\u6210\u7684\u5177\u4f53\u6c47\u7f16\u4ee3\u7801\u3002 explainshell : \u4f60\u662f\u5426\u66fe\u4e3a\u4e00\u6bb5 shell \u4ee3\u7801\u7684\u5177\u4f53\u542b\u4e49\u611f\u5230\u56f0\u6270\uff1fmanpage \u770b\u534a\u5929\u8fd8\u662f\u4e0d\u660e\u6240\u4ee5\uff1f\u8bd5\u8bd5\u8fd9\u4e2a\u7f51\u7ad9\uff01 regex101 : \u6b63\u5219\u8868\u8fbe\u5f0f\u8c03\u8bd5\u7f51\u7ad9\uff0c\u652f\u6301\u5404\u79cd\u7f16\u7a0b\u8bed\u8a00\u7684\u5339\u914d\u6807\u51c6\u3002 typingtom : \u9488\u5bf9\u7a0b\u5e8f\u5458\u7684\u6253\u5b57\u7ec3\u4e60/\u6d4b\u901f\u7f51\u7ad9\u3002 \u5b66\u4e60\u7f51\u7ad9 HFS : \u5404\u7c7b\u8f6f\u4ef6\u6559\u7a0b\u3002 os-wiki : \u64cd\u4f5c\u7cfb\u7edf\u6280\u672f\u8d44\u6e90\u767e\u79d1\u5168\u4e66\u3002 Shadertoy : \u7f16\u5199\u5404\u5f0f\u5404\u6837\u7684 shader\u3002 \u6742\u9879 tophub : \u65b0\u95fb\u70ed\u699c\u5408\u96c6\uff08\u7efc\u5408\u4e86\u77e5\u4e4e\u3001\u5fae\u535a\u3001\u767e\u5ea6\u3001\u5fae\u4fe1\u7b49\uff09\u3002 speedtest : \u5728\u7ebf\u7f51\u7edc\u6d4b\u901f\u7f51\u7ad9\u3002 public-apis : \u516c\u5171 API \u5408\u96c6\u5217\u8868\u3002","title":"\u5b9e\u7528\u5de5\u5177\u7bb1"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_1","text":"","title":"\u5b9e\u7528\u5de5\u5177\u7bb1"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_2","text":"Libgen : PDF\u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9\u3002 z-epub : ePub\u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9\u3002 bitdownloader : \u6cb9\u7ba1\u89c6\u9891\u4e0b\u8f7d\u5668\u3002 zlibrary : \u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9(\u53ef\u80fd\u9700\u8981\u7ffb\u5899)\u3002","title":"\u4e0b\u8f7d\u5de5\u5177"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_3","text":"excalidraw : \u4e00\u6b3e\u624b\u7ed8\u98ce\u683c\u7684\u7ed8\u56fe\u5de5\u5177\uff0c\u975e\u5e38\u9002\u5408\u7ed8\u5236\u8bfe\u7a0b\u62a5\u544a\u6216\u8005PPT\u5185\u7684\u793a\u610f\u56fe\u3002 origamiway : \u624b\u628a\u624b\u6559\u4f60\u600e\u4e48\u6298\u7eb8\u3002 thingiverse : \u56ca\u62ec\u5404\u7c7b 2D/3D \u8bbe\u8ba1\u8d44\u6e90\uff0c\u5176 STL \u6587\u4ef6\u4e0b\u8f7d\u53ef\u76f4\u63a5 3D \u6253\u5370\u3002 iconfont : \u56fd\u5185\u6700\u5927\u7684\u56fe\u6807\u548c\u63d2\u753b\u8d44\u6e90\u5e93\uff0c\u53ef\u7528\u4e8e\u5f00\u53d1\u6216\u7ed8\u5236\u7cfb\u7edf\u67b6\u6784\u56fe\u3002 turbosquid : \u53ef\u4ee5\u8d2d\u4e70\u5404\u5f0f\u5404\u6837\u7684\u6a21\u578b\u3002","title":"\u8bbe\u8ba1\u5de5\u5177"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_4","text":"sqlfiddle : \u4e00\u4e2a\u7b80\u6613\u7684\u5728\u7ebf SQL Playground\u3002 godbolt : \u975e\u5e38\u65b9\u4fbf\u7684\u7f16\u8bd1\u5668\u63a2\u7d22\u5de5\u5177\u3002\u4f60\u53ef\u4ee5\u5199\u4e00\u6bb5 C/C++ \u4ee3\u7801\uff0c\u9009\u62e9\u4e00\u6b3e\u7f16\u8bd1\u5668\uff0c\u7136\u540e\u4fbf\u53ef\u4ee5\u89c2\u5bdf\u751f\u6210\u7684\u5177\u4f53\u6c47\u7f16\u4ee3\u7801\u3002 explainshell : \u4f60\u662f\u5426\u66fe\u4e3a\u4e00\u6bb5 shell \u4ee3\u7801\u7684\u5177\u4f53\u542b\u4e49\u611f\u5230\u56f0\u6270\uff1fmanpage \u770b\u534a\u5929\u8fd8\u662f\u4e0d\u660e\u6240\u4ee5\uff1f\u8bd5\u8bd5\u8fd9\u4e2a\u7f51\u7ad9\uff01 regex101 : \u6b63\u5219\u8868\u8fbe\u5f0f\u8c03\u8bd5\u7f51\u7ad9\uff0c\u652f\u6301\u5404\u79cd\u7f16\u7a0b\u8bed\u8a00\u7684\u5339\u914d\u6807\u51c6\u3002 typingtom : \u9488\u5bf9\u7a0b\u5e8f\u5458\u7684\u6253\u5b57\u7ec3\u4e60/\u6d4b\u901f\u7f51\u7ad9\u3002","title":"\u7f16\u7a0b\u76f8\u5173"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_5","text":"HFS : \u5404\u7c7b\u8f6f\u4ef6\u6559\u7a0b\u3002 os-wiki : \u64cd\u4f5c\u7cfb\u7edf\u6280\u672f\u8d44\u6e90\u767e\u79d1\u5168\u4e66\u3002 Shadertoy : \u7f16\u5199\u5404\u5f0f\u5404\u6837\u7684 shader\u3002","title":"\u5b66\u4e60\u7f51\u7ad9"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_6","text":"tophub : \u65b0\u95fb\u70ed\u699c\u5408\u96c6\uff08\u7efc\u5408\u4e86\u77e5\u4e4e\u3001\u5fae\u535a\u3001\u767e\u5ea6\u3001\u5fae\u4fe1\u7b49\uff09\u3002 speedtest : \u5728\u7ebf\u7f51\u7edc\u6d4b\u901f\u7f51\u7ad9\u3002 public-apis : \u516c\u5171 API \u5408\u96c6\u5217\u8868\u3002","title":"\u6742\u9879"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/%E7%BF%BB%E5%A2%99/","text":"\u7ffb\u5899 \u6b64\u94fe\u63a5 \u51fa\u73b0\u5728\u8fd9\u91cc\u7eaf\u5c5e\u4e8c\u8fdb\u5236 bit \u7684\u968f\u610f\u7ec4\u5408\uff0c\u4e0e\u672c\u4eba\u6beb\u65e0\u5173\u7cfb\u3002","title":"\u7ffb\u5899"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/%E7%BF%BB%E5%A2%99/#_1","text":"\u6b64\u94fe\u63a5 \u51fa\u73b0\u5728\u8fd9\u91cc\u7eaf\u5c5e\u4e8c\u8fdb\u5236 bit \u7684\u968f\u610f\u7ec4\u5408\uff0c\u4e0e\u672c\u4eba\u6beb\u65e0\u5173\u7cfb\u3002","title":"\u7ffb\u5899"},{"location":"%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/CS162/","text":"CS162: Operating System \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A, CS61B, CS61C \u7f16\u7a0b\u8bed\u8a00\uff1aC, x86\u6c47\u7f16 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a200 \u5c0f\u65f6+\uff0c\u4e0a\u4e0d\u5c01\u9876 \u8fd9\u95e8\u8bfe\u8ba9\u6211\u8bb0\u5fc6\u72b9\u65b0\u7684\u6709\u4e24\u4e2a\u90e8\u5206\uff1a \u9996\u5148\u662f\u6559\u6750\uff0c\u8fd9\u672c\u4e66\u7528\u7684\u6559\u6750 Operating Systems: Principles and Practice (2nd Edition) \u4e00\u5171\u56db\u5377\uff0c\u5199\u5f97\u975e\u5e38\u6df1\u5165\u6d45\u51fa\uff0c\u5f88\u597d\u5730\u5f25\u8865\u4e86 MIT6.S081 \u5728\u7406\u8bba\u77e5\u8bc6\u4e0a\u7684\u4e9b\u8bb8\u7a7a\u767d\uff0c\u975e\u5e38\u5efa\u8bae\u5927\u5bb6\u9605\u8bfb\u3002\u76f8\u5173\u8d44\u6e90\u4f1a\u5206\u4eab\u5728\u672c\u4e66\u7684\u7ecf\u5178\u4e66\u7c4d\u63a8\u8350\u6a21\u5757\u3002 \u5176\u6b21\u662f\u8fd9\u95e8\u8bfe\u7684 Project \u2014\u2014 Pintos\u3002Pintos \u662f\u7531 Ben Pfaff \u7b49\u4eba\u5728 x86 \u5e73\u53f0\u4e0a\u7f16\u5199\u7684\u6559\u5b66\u7528\u64cd\u4f5c\u7cfb\u7edf\uff0cBen Pfaff \u751a\u81f3\u4e13\u95e8\u53d1\u4e86\u7bc7 paper \u6765\u9610\u8ff0 Pintos \u7684\u8bbe\u8ba1\u601d\u60f3\u3002 \u548c MIT \u7684 xv6 \u5c0f\u800c\u7cbe\u7684 lab \u8bbe\u8ba1\u7406\u5ff5\u4e0d\u540c\uff0cPintos \u66f4\u6ce8\u91cd\u7cfb\u7edf\u7684 Design and Implementation\u3002Pintos \u672c\u8eab\u4ec5\u4e00\u4e07\u884c\u5de6\u53f3\uff0c\u53ea\u63d0\u4f9b\u4e86\u64cd\u4f5c\u7cfb\u7edf\u6700\u57fa\u672c\u7684\u529f\u80fd\u3002\u800c 4 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\u4e4b\u540e\u6df1\u611f\u6127\u759a\uff0c\u542b\u6cea\u82b1\u4e86\u4e24\u767e\u591a\u4e70\u4e86\u4e00\u672c\u82f1\u6587\u6b63\u7248\u6536\u85cf\u3002\u4e0b\u9762\u9644\u4e0a\u6b64\u4e66\u5c01\u9762\uff0c\u5982\u679c\u4f60\u80fd\u5b8c\u5168\u7406\u89e3\u5c01\u9762\u56fe\u7684\u6570\u5b66\u542b\u4e49\uff0c\u90a3\u4f60\u5bf9\u7ebf\u6027\u4ee3\u6570\u7684\u7406\u89e3\u4e00\u5b9a\u4f1a\u8fbe\u5230\u65b0\u7684\u9ad8\u5ea6\u3002 \u914d\u5408\u6cb9\u7ba1\u6570\u5b66\u7f51\u7ea2 3Blue1Brown \u7684 \u7ebf\u6027\u4ee3\u6570\u7684\u672c\u8d28 \u7cfb\u5217\u89c6\u9891\u98df\u7528\u66f4\u4f73\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/syllabus/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1aIntroduction to Linear Algebra. Gilbert Strang \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9","title":"MIT18.06: Linear Algebra"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITLA/#mit1806-linear-algebra","text":"","title":"MIT18.06: Linear Algebra"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITLA/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u82f1\u6587 \u7f16\u7a0b\u8bed\u8a00\uff1a\u65e0 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a\u56e0\u4eba\u800c\u5f02 \u6570\u5b66\u5927\u725b Gilbert Strang \u8001\u5148\u751f\u5e74\u903e\u53e4\u7a00\u4ecd\u575a\u6301\u6388\u8bfe\uff0c\u5176\u7ecf\u5178\u6559\u6750 Introduction to Linear Algebra \u5df2\u88ab\u6e05\u534e\u91c7\u7528\u4e3a\u5b98\u65b9\u6559\u6750\u3002\u6211\u5f53\u65f6\u770b\u5b8c\u76d7\u7248 PDF \u4e4b\u540e\u6df1\u611f\u6127\u759a\uff0c\u542b\u6cea\u82b1\u4e86\u4e24\u767e\u591a\u4e70\u4e86\u4e00\u672c\u82f1\u6587\u6b63\u7248\u6536\u85cf\u3002\u4e0b\u9762\u9644\u4e0a\u6b64\u4e66\u5c01\u9762\uff0c\u5982\u679c\u4f60\u80fd\u5b8c\u5168\u7406\u89e3\u5c01\u9762\u56fe\u7684\u6570\u5b66\u542b\u4e49\uff0c\u90a3\u4f60\u5bf9\u7ebf\u6027\u4ee3\u6570\u7684\u7406\u89e3\u4e00\u5b9a\u4f1a\u8fbe\u5230\u65b0\u7684\u9ad8\u5ea6\u3002 \u914d\u5408\u6cb9\u7ba1\u6570\u5b66\u7f51\u7ea2 3Blue1Brown \u7684 \u7ebf\u6027\u4ee3\u6570\u7684\u672c\u8d28 \u7cfb\u5217\u89c6\u9891\u98df\u7528\u66f4\u4f73\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITLA/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/syllabus/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1aIntroduction to Linear Algebra. Gilbert Strang \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/","text":"MIT Calculus Course \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u82f1\u8bed \u7f16\u7a0b\u8bed\u8a00\uff1a\u65e0 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a\u56e0\u4eba\u800c\u5f02 MIT \u7684\u5fae\u79ef\u5206\u8bfe\u7531 MIT18.01: Single Variable Calculus \u548c MIT18.02: Multivariable Calculus \u4e24\u95e8\u8bfe\u7ec4\u6210\u3002\u5bf9\u81ea\u5df1\u6570\u5b66\u57fa\u7840\u6bd4\u8f83\u81ea\u4fe1\u7684\u540c\u5b66\u53ef\u4ee5\u53ea\u770b\u8bfe\u7a0b notes\uff0c\u5199\u5f97\u975e\u5e38\u6d45\u663e\u751f\u52a8\u5e76\u4e14\u6293\u4f4f\u672c\u8d28\uff0c\u8ba9\u4f60\u4e0d\u518d\u75b2\u4e8e\u505a\u9898\u800c\u662f\u80fd\u591f\u771f\u6b63\u7aa5\u89c1\u5fae\u79ef\u5206\u7684\u672c\u8d28\u9b45\u529b\u3002 \u914d\u5408\u6cb9\u7ba1\u6570\u5b66\u7f51\u7ea2 3Blue1Brown \u7684 \u5fae\u79ef\u5206\u7684\u672c\u8d28 \u7cfb\u5217\u89c6\u9891\u98df\u7528\u66f4\u4f73\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a 18.01 , 18.02 \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a\u53c2\u89c1\u8bfe\u7a0b notes \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u4e66\u9762\u4f5c\u4e1a\u53ca\u7b54\u6848\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9","title":"MIT18.01/18.02: Calculus"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/#mit-calculus-course","text":"","title":"MIT Calculus Course"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u82f1\u8bed \u7f16\u7a0b\u8bed\u8a00\uff1a\u65e0 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a\u56e0\u4eba\u800c\u5f02 MIT \u7684\u5fae\u79ef\u5206\u8bfe\u7531 MIT18.01: Single Variable Calculus \u548c MIT18.02: Multivariable Calculus \u4e24\u95e8\u8bfe\u7ec4\u6210\u3002\u5bf9\u81ea\u5df1\u6570\u5b66\u57fa\u7840\u6bd4\u8f83\u81ea\u4fe1\u7684\u540c\u5b66\u53ef\u4ee5\u53ea\u770b\u8bfe\u7a0b notes\uff0c\u5199\u5f97\u975e\u5e38\u6d45\u663e\u751f\u52a8\u5e76\u4e14\u6293\u4f4f\u672c\u8d28\uff0c\u8ba9\u4f60\u4e0d\u518d\u75b2\u4e8e\u505a\u9898\u800c\u662f\u80fd\u591f\u771f\u6b63\u7aa5\u89c1\u5fae\u79ef\u5206\u7684\u672c\u8d28\u9b45\u529b\u3002 \u914d\u5408\u6cb9\u7ba1\u6570\u5b66\u7f51\u7ea2 3Blue1Brown \u7684 \u5fae\u79ef\u5206\u7684\u672c\u8d28 \u7cfb\u5217\u89c6\u9891\u98df\u7528\u66f4\u4f73\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a 18.01 , 18.02 \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a\u53c2\u89c1\u8bfe\u7a0b notes \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u4e66\u9762\u4f5c\u4e1a\u53ca\u7b54\u6848\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/information/","text":"MIT6.050J: Information theory and Entropy \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u65e0 \u7f16\u7a0b\u8bed\u8a00\uff1a\u65e0 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 MIT \u9762\u5411\u5927\u4e00\u65b0\u751f\u7684\u4fe1\u606f\u8bba\u5165\u95e8\u8bfe\u7a0b\uff0cPenfield \u6559\u6388\u4e13\u95e8\u4e3a\u8fd9\u95e8\u8bfe\u5199\u4e86\u4e00\u672c \u6559\u6750 \u4f5c\u4e3a\u8bfe\u7a0b notes\uff0c\u5185\u5bb9\u6df1\u5165\u6d45\u51fa\uff0c\u751f\u52a8\u6709\u8da3\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/index.htm \u8bfe\u7a0b\u6559\u6750\uff1a https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/syllabus/MIT6_050JS08_textbook.pdf \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9\uff0c\u5305\u542b\u4e66\u9762\u4f5c\u4e1a\u4e0e Matlab \u7f16\u7a0b\u4f5c\u4e1a\u3002","title":"MIT6.050J: Information theory and Entropy"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/information/#mit6050j-information-theory-and-entropy","text":"","title":"MIT6.050J: Information theory and Entropy"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/information/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u65e0 \u7f16\u7a0b\u8bed\u8a00\uff1a\u65e0 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 MIT \u9762\u5411\u5927\u4e00\u65b0\u751f\u7684\u4fe1\u606f\u8bba\u5165\u95e8\u8bfe\u7a0b\uff0cPenfield \u6559\u6388\u4e13\u95e8\u4e3a\u8fd9\u95e8\u8bfe\u5199\u4e86\u4e00\u672c \u6559\u6750 \u4f5c\u4e3a\u8bfe\u7a0b notes\uff0c\u5185\u5bb9\u6df1\u5165\u6d45\u51fa\uff0c\u751f\u52a8\u6709\u8da3\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/information/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/index.htm \u8bfe\u7a0b\u6559\u6750\uff1a https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/syllabus/MIT6_050JS08_textbook.pdf \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9\uff0c\u5305\u542b\u4e66\u9762\u4f5c\u4e1a\u4e0e Matlab \u7f16\u7a0b\u4f5c\u4e1a\u3002","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/6.042J/","text":"MIT 6.042J: Mathematics for Computer Science \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1aCalculus, Linear Algebra \u7f16\u7a0b\u8bed\u8a00\uff1aPython preferred \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a50-70 \u5c0f\u65f6 MIT \u7684\u79bb\u6563\u6570\u5b66\u4ee5\u53ca\u6982\u7387\u7efc\u5408\u8bfe\u7a0b\uff0c\u5bfc\u5e08\u662f\u5927\u540d\u9f0e\u9f0e\u7684 Tom Leighton ( Akamai \u7684\u8054\u5408\u521b\u59cb\u4eba\u4e4b\u4e00)\u3002\u5b66\u5b8c\u4e4b\u540e\u5bf9\u4e8e\u540e\u7eed\u7684\u7b97\u6cd5\u5b66\u4e60\u5927\u6709\u88e8\u76ca\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1L741147VX \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/assignments/","title":"MIT 6.042J: Mathematics for Computer Science"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/6.042J/#mit-6042j-mathematics-for-computer-science","text":"","title":"MIT 6.042J: Mathematics for Computer Science"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/6.042J/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1aCalculus, Linear Algebra \u7f16\u7a0b\u8bed\u8a00\uff1aPython preferred \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a50-70 \u5c0f\u65f6 MIT \u7684\u79bb\u6563\u6570\u5b66\u4ee5\u53ca\u6982\u7387\u7efc\u5408\u8bfe\u7a0b\uff0c\u5bfc\u5e08\u662f\u5927\u540d\u9f0e\u9f0e\u7684 Tom Leighton ( Akamai 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\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://www.inference.org.uk/mackay/itila/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1rs411T71e \u8bfe\u7a0b\u6559\u6750\uff1aInformation Theory, Inference, and Learning Algorithms \u5728\u8bfe\u7a0b\u7f51\u7ad9\u53ef\u4ee5\u4e0b\u8f7d\u5230\u514d\u8d39\u7684\u7535\u5b50\u7248 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u5728\u6bcf\u4e00\u8282\u8bfe\u89c6\u9891\u7684\u6700\u540e\u4f1a\u7559\u6559\u6750\u4e0a\u7684\u8bfe\u540e\u4e60\u9898 R.I.P Prof. David MacKay","title":"The Information Theory, Patter Recognition, and Neural Networks"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/#the-information-theory-patter-recognition-and-neural-networks","text":"","title":"The Information Theory, Patter Recognition, and Neural Networks"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aCambridge \u5148\u4fee\u8981\u6c42\uff1aCalculus, Linear Algebra, Probabilities and Statistics \u7f16\u7a0b\u8bed\u8a00\uff1aAnything would be OK, Python preferred \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a30-50 \u5c0f\u65f6 \u5251\u6865\u5927\u5b66 Sir David MacKay \u6559\u6388\u7684\u4fe1\u606f\u8bba\u8bfe\u7a0b\u3002\u6559\u6388\u662f\u4e00\u4f4d\u5341\u5206\u7cbe\u901a\u4fe1\u606f\u8bba\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u5b66\u8005\uff0c\u8bfe\u7a0b\u5bf9\u5e94\u6559\u6750\u4e5f\u662f\u4fe1\u606f\u8bba\u9886\u57df\u7684\u4e00\u90e8\u7ecf\u5178\u8457\u4f5c\u3002\u53ef\u60dc\u5929\u5992\u82f1\u624d...","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://www.inference.org.uk/mackay/itila/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1rs411T71e \u8bfe\u7a0b\u6559\u6750\uff1aInformation Theory, Inference, and Learning Algorithms \u5728\u8bfe\u7a0b\u7f51\u7ad9\u53ef\u4ee5\u4e0b\u8f7d\u5230\u514d\u8d39\u7684\u7535\u5b50\u7248 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\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://stanford.edu/class/ee364a/index.html \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1aD4y1Q7aW \u8bfe\u7a0b\u6559\u6750\uff1a Convex Optimization \u8bfe\u7a0b\u4f5c\u4e1a\uff1a9 \u4e2a Python \u7f16\u7a0b\u4f5c\u4e1a \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/Standford_CVX101 - GitHub \u4e2d\u3002","title":"Standford EE364A: Convex Optimization"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/#stanford-ee364a-convex-optimization","text":"","title":"Stanford EE364A: Convex Optimization"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1aPython\uff0c\u5fae\u79ef\u5206\uff0c\u7ebf\u6027\u4ee3\u6570\uff0c\u6982\u7387\u8bba\uff0c\u6570\u503c\u5206\u6790 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 Stephen Boyd \u6559\u6388\u662f\u51f8\u4f18\u5316\u9886\u57df\u7684\u5927\u725b\uff0c\u5176\u7f16\u5199\u7684 Convex Optimization \u8fd9\u672c\u6559\u6750\u88ab\u4f17\u591a\u540d\u6821\u91c7\u7528\u3002\u53e6\u5916\u5176\u7814\u7a76\u56e2\u961f\u8fd8\u4e13\u95e8\u5f00\u53d1\u4e86\u4e00\u4e2a\u7528\u4e8e\u6c42\u89e3\u5e38\u89c1\u51f8\u4f18\u5316\u95ee\u9898\u7684\u7f16\u7a0b\u6846\u67b6\uff0c\u652f\u6301 Python, Julia \u7b49\u4e3b\u6d41\u7f16\u7a0b\u8bed\u8a00\uff0c\u5176\u8bfe\u7a0b\u4f5c\u4e1a\u4e5f\u662f\u91c7\u7528\u8fd9\u4e2a\u7f16\u7a0b\u6846\u67b6\u53bb\u89e3\u51b3\u5b9e\u9645\u751f\u6d3b\u5f53\u4e2d\u7684\u51f8\u4f18\u5316\u95ee\u9898\u3002 \u5728\u5b9e\u9645\u8fd0\u7528\u5f53\u4e2d\uff0c\u4f60\u4f1a\u6df1\u523b\u4f53\u4f1a\u5230\u5bf9\u4e8e\u540c\u4e00\u4e2a\u95ee\u9898\uff0c\u5efa\u6a21\u8fc7\u7a0b\u4e2d\u4e00\u4e2a\u7ec6\u5c0f\u7684\u6539\u53d8\uff0c\u5176\u65b9\u7a0b\u7684\u6c42\u89e3\u96be\u5ea6\u4f1a\u6709\u5929\u58e4\u4e4b\u522b\uff0c\u5982\u4f55\u8ba9\u4f60\u5efa\u6a21\u7684\u65b9\u7a0b\u662f\u201c\u51f8\u201d\u7684\uff0c\u662f\u4e00\u95e8\u827a\u672f\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://stanford.edu/class/ee364a/index.html \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1aD4y1Q7aW \u8bfe\u7a0b\u6559\u6750\uff1a Convex Optimization \u8bfe\u7a0b\u4f5c\u4e1a\uff1a9 \u4e2a Python \u7f16\u7a0b\u4f5c\u4e1a","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/#_3","text":"@PKUFlyingPig 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Join\uff0c\u7edf\u8ba1\u4fe1\u606f\u4ee5\u53ca\u4ee3\u4ef7\u4f30\u8ba1\uff0c\u5b50\u67e5\u8be2\u5b9e\u73b0\uff0cAgg\uff0cGroup By \u7684\u5b9e\u73b0\u7b49\u3002\u9664\u6b64\u4e4b\u5916\uff0c\u8fd8\u6709 B+\u6811\uff0cWAL \u76f8\u5173\u5b9e\u9a8c\u3002\u672c\u95e8\u8bfe\u7a0b\u9002\u5408\u5728\u5b66\u5b8c CMU15-445 \u8bfe\u7a0b\u4e4b\u540e\uff0c\u5bf9\u67e5\u8be2\u4f18\u5316\u76f8\u5173\u5185\u5bb9\u6709\u5174\u8da3\u7684\u540c\u5b66\u3002 \u4e0b\u9762\u4ecb\u7ecd\u4e00\u4e0b\u8fd9\u95e8\u8bfe\u7684\u524d 3 \u4e2a Assignment \u4e5f\u5c31\u662f\u5b9e\u9a8c Lab \u6240\u8981\u5b9e\u73b0\u7684\u529f\u80fd\uff1a Assignment1 \u4e3a NanoDB \u63d0\u4f9b delete\uff0cupdate \u8bed\u53e5\u7684\u652f\u6301\u3002 \u4e3a Buffer Pool Manager \u6dfb\u52a0\u5408\u9002\u7684 pin/unpin \u4ee3\u7801\u3002 \u63d0\u5347 insert \u8bed\u53e5\u7684\u6027\u80fd\uff0c \u540c\u65f6\u4e0d\u4f7f\u6570\u636e\u5e93\u6587\u4ef6\u5927\u5c0f\u8fc7\u5206\u81a8\u80c0\u3002 Assignment2 \u5b9e\u73b0\u4e00\u4e2a\u7b80\u5355\u7684\u8ba1\u5212\u751f\u6210\u5668\uff0c\u5c06\u5404\u79cd\u5df2\u7ecf Parser \u8fc7\u7684 SQL \u8bed\u53e5\u8f6c\u5316\u4e3a\u53ef\u6267\u884c\u7684\u6267\u884c\u8ba1\u5212\u3002 \u4f7f\u7528 nested-loop join \u7b97\u6cd5\uff0c\u5b9e\u73b0\u652f\u6301 inner- and outer-join \u7684 Join \u8ba1\u5212\u8282\u70b9\u3002 \u6dfb\u52a0\u4e00\u4e9b\u5355\u5143\u6d4b\u8bd5\uff0c \u4fdd\u8bc1 inner- and outer-join \u529f\u80fd\u5b9e\u73b0\u6b63\u786e\u3002 Assignment3 \u5b8c\u6210\u6536\u96c6\u8868\u7684\u7edf\u8ba1\u4fe1\u606f\u3002 \u5b8c\u6210\u5404\u79cd\u8ba1\u5212\u8282\u70b9\u7684\u8ba1\u5212\u6210\u672c\u8ba1\u7b97\u3002 \u8ba1\u7b97\u53ef\u51fa\u73b0\u5728\u6267\u884c\u8ba1\u5212\u4e2d\u7684\u5404\u79cd\u8c13\u8bcd\u7684\u9009\u62e9\u6027\u3002 \u6839\u636e\u8c13\u8bcd\u66f4\u65b0\u8ba1\u5212\u8282\u70b9\u8f93\u51fa\u7684\u5143\u7ec4\u7edf\u8ba1\u4fe1\u606f\u3002 \u5269\u4f59 Assignment \u548c Challenges \u53ef\u4ee5\u67e5\u770b\u8bfe\u7a0b\u4ecb\u7ecd\uff0c\u63a8\u8350\u4f7f\u7528 IDEA \u6253\u5f00\u5de5\u7a0b\uff0cMaven \u6784\u5efa\uff0c\u6ce8\u610f\u65e5\u5fd7\u76f8\u5173\u914d\u7f6e\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://courses.cms.caltech.edu/cs122/ \u8bfe\u7a0b\u4ee3\u7801\uff1a https://gitlab.caltech.edu/cs122-19wi \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a7 Assignments + 2 Challenges","title":"Caltech CS122: Database System Implementation"},{"location":"%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS122/#caltech-cs-122-database-system-implementation","text":"","title":"Caltech CS 122: Database System Implementation"},{"location":"%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS122/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aCaltech \u5148\u4fee\u8981\u6c42\uff1a\u65e0 \u7f16\u7a0b\u8bed\u8a00\uff1aJava \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f 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\u8fd9\u95e8\u8bfe\u7684\u6784\u6210\u5c31\u975e\u5e38\u597d\u5730\u5951\u5408\u4e86\u4e0a\u8ff0\u4e09\u4e2a\u6b65\u9aa4\u3002\u89c2\u770b\u8bfe\u7a0b\u89c6\u9891\u5e76\u4e14\u9605\u8bfb\u6559\u6388\u7684 \u5f00\u6e90\u8bfe\u672c \u6709\u52a9\u4e8e\u4f60\u7406\u89e3\u7b97\u6cd5\u7684\u672c\u8d28\uff0c\u8ba9\u4f60\u4e5f\u53ef\u4ee5\u7528\u975e\u5e38 \u751f\u52a8\u6d45\u663e\u7684\u8bdd\u8bed\u5411\u522b\u4eba\u8bb2\u8ff0\u4e3a\u4ec0\u4e48\u8fd9\u4e2a\u7b97\u6cd5\u5f97\u957f\u8fd9\u4e2a\u6837\u5b50\u3002 \u5728\u7406\u89e3\u7b97\u6cd5\u4e4b\u540e\uff0c\u4f60\u53ef\u4ee5\u9605\u8bfb\u6559\u6388\u5bf9\u4e8e\u8bfe\u7a0b\u4e2d\u8bb2\u6388\u7684\u6240\u6709\u6570\u636e\u7ed3\u6784\u4e0e\u7b97\u6cd5\u7684 \u4ee3\u7801\u5b9e\u73b0 \u3002 \u6ce8\u610f\uff0c\u8fd9\u4e9b\u5b9e\u73b0\u53ef\u4e0d\u662f demo \u6027\u8d28\u7684\uff0c\u800c\u662f\u5de5\u4e1a\u7ea7\u7684\u9ad8\u6548\u5b9e\u73b0\uff0c\u4ece\u6ce8\u91ca\u5230\u53d8\u91cf\u547d\u540d\u90fd\u975e\u5e38\u4e25\u8c28\uff0c\u6a21\u5757\u5316\u4e5f\u505a\u5f97\u76f8\u5f53\u597d\uff0c\u662f\u8d28\u91cf\u5f88\u9ad8\u7684\u4ee3\u7801\u3002\u6211\u4ece\u8fd9\u4e9b\u4ee3\u7801\u4e2d\u6536\u83b7\u826f\u591a\u3002 \u6700\u540e\uff0c\u5c31\u662f\u8fd9\u95e8\u8bfe\u6700\u6fc0\u52a8\u4eba\u5fc3\u7684\u90e8\u5206\u4e86\uff0c10 \u4e2a\u9ad8\u8d28\u91cf\u7684 Project\uff0c\u5e76\u4e14\u5168\u90fd\u6709\u5b9e\u9645\u95ee\u9898\u7684\u80cc\u666f\u63cf\u8ff0\uff0c\u4e30\u5bcc\u7684\u6d4b\u8bd5\u6837\u4f8b\uff0c\u81ea\u52a8\u7684\u8bc4\u5206\u7cfb\u7edf\uff08\u4ee3\u7801\u98ce\u683c\u4e5f\u662f\u8bc4\u5206\u7684\u4e00\u73af\uff09\u3002\u8ba9\u4f60\u5728\u5b9e\u9645\u751f\u6d3b\u4e2d \u9886\u7565\u7b97\u6cd5\u7684\u9b45\u529b\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a Algorithm I , Algorithm II \u8bfe\u7a0b\u89c6\u9891\uff1a\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a https://algs4.cs.princeton.edu/home/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a10\u4e2aProject\uff0c\u5177\u4f53\u8981\u6c42\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/Princeton-Algorithm - GitHub \u4e2d\u3002","title":"Coursera: Algorithms I & II"},{"location":"%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/#coursera-algorithms-i-ii","text":"","title":"Coursera: Algorithms I & II"},{"location":"%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aPrinceton \u5148\u4fee\u8981\u6c42\uff1aCS61A \u7f16\u7a0b\u8bed\u8a00\uff1aJava \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a60 \u5c0f\u65f6 \u8fd9\u662f Coursera \u4e0a\u8bc4\u5206\u6700\u9ad8\u7684\u7b97\u6cd5\u8bfe\u7a0b\u3002Robert Sedgewick \u6559\u6388\u6709\u4e00\u79cd\u9b54\u529b\uff0c\u53ef\u4ee5\u5c06\u65e0\u8bba\u591a\u4e48\u590d\u6742\u7684\u7b97\u6cd5\u8bb2\u5f97\u6781\u4e3a\u751f\u52a8\u6d45\u663e\u3002\u5b9e\u4e0d\u76f8\u7792\uff0c\u56f0\u6270\u6211\u591a\u5e74\u7684 KMP \u4ee5\u53ca\u7f51\u7edc\u6d41\u7b97\u6cd5\u90fd\u662f\u5728\u8fd9\u95e8\u8bfe\u4e0a\u8ba9\u6211\u8305\u585e\u987f\u5f00\u7684\uff0c\u65f6\u9694\u4e24\u5e74\u6211\u751a\u81f3\u8fd8\u80fd\u5199\u51fa\u8fd9\u4e24\u4e2a\u7b97\u6cd5\u7684\u63a8\u5bfc\u4e0e\u8bc1\u660e\u3002 \u4f60\u662f\u5426\u89c9\u5f97\u7b97\u6cd5\u5b66\u4e86\u5c31\u5fd8\u5462\uff1f\u6211\u89c9\u5f97\u8ba9\u4f60\u5b8c\u5168\u638c\u63e1\u4e00\u4e2a\u7b97\u6cd5\u7684\u6838\u5fc3\u5728\u4e8e\u7406\u89e3\u4e09\u70b9\uff1a \u4e3a\u4ec0\u4e48\u8fd9\u4e48\u505a\uff1f\uff08\u6b63\u786e\u6027\u63a8\u5bfc\uff0c\u6291\u6216\u662f\u6574\u4e2a\u7b97\u6cd5\u7684\u6838\u5fc3\u672c\u8d28\uff09 \u5982\u4f55\u5b9e\u73b0\u5b83\uff1f\uff08\u5149\u5b66\u4e0d\u7528\u5047\u628a\u5f0f\uff09 \u7528\u5b83\u89e3\u51b3\u5b9e\u9645\u95ee\u9898\uff08\u5b66\u4ee5\u81f4\u7528\u624d\u662f\u771f\u672c\u4e8b\uff09 \u8fd9\u95e8\u8bfe\u7684\u6784\u6210\u5c31\u975e\u5e38\u597d\u5730\u5951\u5408\u4e86\u4e0a\u8ff0\u4e09\u4e2a\u6b65\u9aa4\u3002\u89c2\u770b\u8bfe\u7a0b\u89c6\u9891\u5e76\u4e14\u9605\u8bfb\u6559\u6388\u7684 \u5f00\u6e90\u8bfe\u672c \u6709\u52a9\u4e8e\u4f60\u7406\u89e3\u7b97\u6cd5\u7684\u672c\u8d28\uff0c\u8ba9\u4f60\u4e5f\u53ef\u4ee5\u7528\u975e\u5e38 \u751f\u52a8\u6d45\u663e\u7684\u8bdd\u8bed\u5411\u522b\u4eba\u8bb2\u8ff0\u4e3a\u4ec0\u4e48\u8fd9\u4e2a\u7b97\u6cd5\u5f97\u957f\u8fd9\u4e2a\u6837\u5b50\u3002 \u5728\u7406\u89e3\u7b97\u6cd5\u4e4b\u540e\uff0c\u4f60\u53ef\u4ee5\u9605\u8bfb\u6559\u6388\u5bf9\u4e8e\u8bfe\u7a0b\u4e2d\u8bb2\u6388\u7684\u6240\u6709\u6570\u636e\u7ed3\u6784\u4e0e\u7b97\u6cd5\u7684 \u4ee3\u7801\u5b9e\u73b0 \u3002 \u6ce8\u610f\uff0c\u8fd9\u4e9b\u5b9e\u73b0\u53ef\u4e0d\u662f demo \u6027\u8d28\u7684\uff0c\u800c\u662f\u5de5\u4e1a\u7ea7\u7684\u9ad8\u6548\u5b9e\u73b0\uff0c\u4ece\u6ce8\u91ca\u5230\u53d8\u91cf\u547d\u540d\u90fd\u975e\u5e38\u4e25\u8c28\uff0c\u6a21\u5757\u5316\u4e5f\u505a\u5f97\u76f8\u5f53\u597d\uff0c\u662f\u8d28\u91cf\u5f88\u9ad8\u7684\u4ee3\u7801\u3002\u6211\u4ece\u8fd9\u4e9b\u4ee3\u7801\u4e2d\u6536\u83b7\u826f\u591a\u3002 \u6700\u540e\uff0c\u5c31\u662f\u8fd9\u95e8\u8bfe\u6700\u6fc0\u52a8\u4eba\u5fc3\u7684\u90e8\u5206\u4e86\uff0c10 \u4e2a\u9ad8\u8d28\u91cf\u7684 Project\uff0c\u5e76\u4e14\u5168\u90fd\u6709\u5b9e\u9645\u95ee\u9898\u7684\u80cc\u666f\u63cf\u8ff0\uff0c\u4e30\u5bcc\u7684\u6d4b\u8bd5\u6837\u4f8b\uff0c\u81ea\u52a8\u7684\u8bc4\u5206\u7cfb\u7edf\uff08\u4ee3\u7801\u98ce\u683c\u4e5f\u662f\u8bc4\u5206\u7684\u4e00\u73af\uff09\u3002\u8ba9\u4f60\u5728\u5b9e\u9645\u751f\u6d3b\u4e2d \u9886\u7565\u7b97\u6cd5\u7684\u9b45\u529b\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a Algorithm I , Algorithm II \u8bfe\u7a0b\u89c6\u9891\uff1a\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a https://algs4.cs.princeton.edu/home/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a10\u4e2aProject\uff0c\u5177\u4f53\u8981\u6c42\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/Princeton-Algorithm - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS170/","text":"CS170: Efficient Algorithms and Intractable Problems \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61B, CS70 \u7f16\u7a0b\u8bed\u8a00\uff1aLaTeX \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a60 \u5c0f\u65f6 \u4f2f\u514b\u5229\u7684\u7b97\u6cd5\u8bbe\u8ba1\u8bfe\uff0c\u66f4\u6ce8\u91cd\u7b97\u6cd5\u7684\u7406\u8bba\u57fa\u7840\u4e0e\u590d\u6742\u5ea6\u5206\u6790\u3002\u8bfe\u7a0b\u5185\u5bb9\u6db5\u76d6\u4e86\u5206\u6cbb\u3001\u56fe\u7b97\u6cd5\u3001\u6700\u77ed\u8def\u3001\u751f\u6210\u6811\u3001\u8d2a\u5fc3\u3001\u52a8\u89c4\u3001\u5e76\u67e5\u96c6\u3001\u7ebf\u6027\u89c4\u5212\u3001\u7f51\u7edc\u6d41\u3001NP \u95ee\u9898\u3001\u968f\u673a\u7b97\u6cd5\u3001\u54c8\u5e0c\u7b97\u6cd5\u7b49\u7b49\u3002 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\u8fd9\u4e00\u4e3b\u6d41\u7684\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u6846\u67b6\u4e3a\u4f8b\uff08\u9648\u5929\u5947\u662f\u8fd9\u4e2a\u6846\u67b6\u7684\u521b\u59cb\u4eba\u4e4b\u4e00\uff09\uff0c\u805a\u7126\u4e8e\u5982\u4f55\u5c06\u5f00\u53d1\u6a21\u5f0f\u4e0b\uff08\u5982 Tensorflow, Pytorch, Jax\uff09\u7684\u5404\u7c7b\u673a\u5668\u5b66\u4e60\u6a21\u578b\uff0c\u901a\u8fc7\u4e00\u5957\u666e\u9002\u7684\u62bd\u8c61\u548c\u4f18\u5316\u7b97\u6cd5\uff0c\u53d8\u6362\u4e3a\u62e5\u6709\u66f4\u9ad8\u6027\u80fd\u5e76\u4e14\u9002\u914d\u5404\u7c7b\u5e95\u5c42\u786c\u4ef6\u7684\u90e8\u7f72\u6a21\u5f0f\u3002\u8bfe\u7a0b\u8bb2\u6388\u7684\u77e5\u8bc6\u70b9\u90fd\u662f\u76f8\u5bf9 High-Level \u7684\u5b8f\u89c2\u6982\u5ff5\uff0c\u540c\u65f6\u6bcf\u8282\u8bfe\u90fd\u4f1a\u6709\u4e00\u4e2a\u914d\u5957\u7684 Jupyter Notebook \u6765\u901a\u8fc7\u5177\u4f53\u7684\u4ee3\u7801\u8bb2\u89e3\u77e5\u8bc6\u70b9\uff0c\u56e0\u6b64\u5982\u679c\u4ece\u4e8b TVM \u76f8\u5173\u7684\u7f16\u7a0b\u5f00\u53d1\u7684\u8bdd\uff0c\u8fd9\u95e8\u8bfe\u6709\u4e30\u5bcc\u4e14\u89c4\u8303\u7684\u4ee3\u7801\u793a\u4f8b\u4ee5\u4f9b\u53c2\u8003\u3002 \u6240\u6709\u7684\u8bfe\u7a0b\u8d44\u6e90\u5168\u90e8\u5f00\u6e90\u5e76\u4e14\u6709\u4e2d\u6587\u548c\u82f1\u6587\u4e24\u4e2a\u7248\u672c\uff0cB\u7ad9\u548c\u6cb9\u7ba1\u5206\u522b\u6709\u4e2d\u6587\u548c\u82f1\u6587\u7684\u8bfe\u7a0b\u5f55\u5f71\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%B3%BB%E7%BB%9F/MLC/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://mlc.ai/summer22-zh/ \u8bfe\u7a0b\u89c6\u9891\uff1a Bilibili \u8bfe\u7a0b\u7b14\u8bb0\uff1a https://mlc.ai/zh/index.html \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://github.com/mlc-ai/notebooks/blob/main/assignment","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CMU10-708/","text":"CMU 10-708: Probabilistic Graphical Models \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aCMU \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://sailinglab.github.io/pgm-spring-2019/ \u8fd9\u4e2a\u7f51\u7ad9\u5305\u542b\u4e86\u6240\u6709\u7684\u8d44\u6e90\uff1aslides, nots, video, homework, project \u8fd9\u95e8\u8bfe\u7a0b\u662f CMU \u7684\u56fe\u6a21\u578b\u57fa\u7840 + \u8fdb\u9636\u8bfe\uff0c\u6388\u8bfe\u8001\u5e08\u4e3a Eric P. Xing\uff0c\u6db5\u76d6\u4e86\u56fe\u6a21\u578b\u57fa\u7840\uff0c\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u5728\u5f3a\u5316\u5b66\u4e60\u4e2d\u7684\u5e94\u7528\uff0c\u4ee5\u53ca\u975e\u53c2\u6570\u65b9\u6cd5\u3002\u76f8\u5f53\u786c\u6838","title":"CMU 10-708: Probabilistic Graphical Models"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CMU10-708/#cmu-10-708-probabilistic-graphical-models","text":"","title":"CMU 10-708: Probabilistic Graphical Models"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CMU10-708/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aCMU \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://sailinglab.github.io/pgm-spring-2019/ \u8fd9\u4e2a\u7f51\u7ad9\u5305\u542b\u4e86\u6240\u6709\u7684\u8d44\u6e90\uff1aslides, nots, video, homework, project \u8fd9\u95e8\u8bfe\u7a0b\u662f CMU \u7684\u56fe\u6a21\u578b\u57fa\u7840 + \u8fdb\u9636\u8bfe\uff0c\u6388\u8bfe\u8001\u5e08\u4e3a Eric P. Xing\uff0c\u6db5\u76d6\u4e86\u56fe\u6a21\u578b\u57fa\u7840\uff0c\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u5728\u5f3a\u5316\u5b66\u4e60\u4e2d\u7684\u5e94\u7528\uff0c\u4ee5\u53ca\u975e\u53c2\u6570\u65b9\u6cd5\u3002\u76f8\u5f53\u786c\u6838","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CS229M/","text":"STATS214 / CS229M: Machine Learning Theory \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Statistics \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/stats214/ \u7ecf\u5178\u5b66\u4e60\u7406\u8bba + \u6700\u65b0\u6df1\u5ea6\u5b66\u4e60\u7406\u8bba\uff0c\u975e\u5e38\u786c\u6838\u3002\u6388\u8bfe\u8001\u5e08\u4e4b\u524d\u662f Percy Liang\uff0c\u73b0\u5728\u662f Tengyu Ma","title":"Stanford STATS214 / CS229M: Machine Learning Theory"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CS229M/#stats214-cs229m-machine-learning-theory","text":"","title":"STATS214 / CS229M: Machine Learning Theory"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CS229M/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Statistics \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/stats214/ \u7ecf\u5178\u5b66\u4e60\u7406\u8bba + \u6700\u65b0\u6df1\u5ea6\u5b66\u4e60\u7406\u8bba\uff0c\u975e\u5e38\u786c\u6838\u3002\u6388\u8bfe\u8001\u5e08\u4e4b\u524d\u662f Percy Liang\uff0c\u73b0\u5728\u662f Tengyu Ma","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STA4273/","text":"STA 4273 Winter 2021: Minimizing Expectations \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aU Toronto \u5148\u4fee\u8981\u6c42\uff1aBayesian Inference, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.cs.toronto.edu/~cmaddis/courses/sta4273_w21/ \u8fd9\u662f\u4e00\u95e8\u8f83\u4e3a\u8fdb\u9636\u7684 Ph.D. \u7814\u7a76\u8bfe\u7a0b\uff0c\u6838\u5fc3\u5185\u5bb9\u662f inference \u548c control \u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u6388\u8bfe\u8001\u5e08\u4e3a Chris Maddison (AlphaGo founding member, NeurIPS 14 best paper)","title":"U Toronto STA 4273 Winter 2021: Minimizing Expectations"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STA4273/#sta-4273-winter-2021-minimizing-expectations","text":"","title":"STA 4273 Winter 2021: Minimizing Expectations"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STA4273/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aU Toronto \u5148\u4fee\u8981\u6c42\uff1aBayesian Inference, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.cs.toronto.edu/~cmaddis/courses/sta4273_w21/ \u8fd9\u662f\u4e00\u95e8\u8f83\u4e3a\u8fdb\u9636\u7684 Ph.D. \u7814\u7a76\u8bfe\u7a0b\uff0c\u6838\u5fc3\u5185\u5bb9\u662f inference \u548c control \u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u6388\u8bfe\u8001\u5e08\u4e3a Chris Maddison (AlphaGo founding member, NeurIPS 14 best paper)","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STAT8201/","text":"Columbia STAT 8201: Deep Generative Models \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aColumbia University \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Graphical Models \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://stat.columbia.edu/~cunningham/teaching/GR8201/ \u8fd9\u95e8\u8bfe\u662f\u4e00\u95e8 PhD \u8ba8\u8bba\u73ed\uff0c\u6bcf\u5468\u7684\u5185\u5bb9\u662f\u5c55\u793a + \u8ba8\u8bba\u8bba\u6587\uff0c\u6388\u8bfe\u8001\u5e08\u662f John Cunningham\u3002Deep Generative Models \uff08\u6df1\u5ea6\u751f\u6210\u6a21\u578b\uff09 \u662f\u56fe\u6a21\u578b\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u4e5f\u662f\u73b0\u4ee3\u673a\u5668\u5b66\u4e60\u6700\u91cd\u8981\u7684\u65b9\u5411\u4e4b\u4e00","title":"Columbia STAT 8201: Deep Generative Models"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STAT8201/#columbia-stat-8201-deep-generative-models","text":"","title":"Columbia STAT 8201: Deep Generative Models"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STAT8201/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aColumbia University \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Graphical Models \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://stat.columbia.edu/~cunningham/teaching/GR8201/ \u8fd9\u95e8\u8bfe\u662f\u4e00\u95e8 PhD \u8ba8\u8bba\u73ed\uff0c\u6bcf\u5468\u7684\u5185\u5bb9\u662f\u5c55\u793a + \u8ba8\u8bba\u8bba\u6587\uff0c\u6388\u8bfe\u8001\u5e08\u662f John Cunningham\u3002Deep Generative Models \uff08\u6df1\u5ea6\u751f\u6210\u6a21\u578b\uff09 \u662f\u56fe\u6a21\u578b\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u4e5f\u662f\u73b0\u4ee3\u673a\u5668\u5b66\u4e60\u6700\u91cd\u8981\u7684\u65b9\u5411\u4e4b\u4e00","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/","text":"\u673a\u5668\u5b66\u4e60\u8fdb\u9636 \u6b64\u8def\u7ebf\u56fe\u9002\u7528\u4e8e\u5df2\u7ecf\u5b66\u8fc7\u4e86\u57fa\u7840\u673a\u5668\u5b66\u4e60 (ML, NLP, CV, RL) \u7684\u540c\u5b66 (\u9ad8\u5e74\u7ea7\u672c\u79d1\u751f\u6216\u4f4e\u5e74\u7ea7\u7814\u7a76\u751f)\uff0c\u5df2\u7ecf\u53d1\u8868\u8fc7\u81f3\u5c11\u4e00\u7bc7\u9876\u4f1a\u8bba\u6587 (NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, CVPR, ICCV) \u60f3\u8981\u8d70\u673a\u5668\u5b66\u4e60\u79d1\u7814\u8def\u7ebf\u7684\u9009\u624b\u3002 \u6b64\u8def\u7ebf\u7684\u76ee\u6807\u662f\u4e3a\u8bfb\u61c2\u4e0e\u53d1\u8868\u673a\u5668\u5b66\u4e60\u9876\u4f1a\u8bba\u6587\u6253\u4e0b\u7406\u8bba\u57fa\u7840\uff0c\u7279\u522b\u662f Probabilistic Methods \u8fd9\u4e2a track \u4e0b\u7684\u6587\u7ae0 \u673a\u5668\u5b66\u4e60\u8fdb\u9636\u53ef\u80fd\u5b58\u5728\u591a\u79cd\u4e0d\u540c\u7684\u5b66\u4e60\u8def\u7ebf\uff0c\u6b64\u8def\u7ebf\u53ea\u80fd\u4ee3\u8868\u4f5c\u8005 Yao Fu \u6240\u7406\u89e3\u7684\u6700\u4f73\u8def\u5f84\uff0c\u4fa7\u91cd\u4e8e\u8d1d\u53f6\u65af\u5b66\u6d3e\u4e0b\u7684\u6982\u7387\u5efa\u6a21\u65b9\u6cd5\uff0c\u4e5f\u4f1a\u6d89\u53ca\u5230\u5404\u9879\u76f8\u5173\u5b66\u79d1\u7684\u4ea4\u53c9\u77e5\u8bc6\u3002 \u5fc5\u8bfb\u6559\u6750 PRML: Pattern Recognition and Machine Learning. Christopher Bishop \u7ecf\u5178\u8d1d\u53f6\u65af\u5b66\u6d3e\u6559\u6750 AoS: All of Statistics. Larry Wasserman \u7ecf\u5178\u9891\u7387\u5b66\u6d3e\u6559\u6750 \u6240\u4ee5\u8fd9\u4e24\u672c\u4e66\u521a\u597d\u76f8\u8f85\u76f8\u6210 \u5b57\u5178 MLAPP: Machine Learning: A Probabilistic Perspective. Kevin Murphy Convex Optimization. Stephen Boyd and Lieven Vandenberghe \u8fdb\u9636\u4e66\u7c4d W&J: Graphical Models, Exponential Families, and Variational Inference. Martin Wainwright and Michael Jordan Theory of Point Estimation. E. L. Lehmann and George Casella \u5982\u4f55\u9605\u8bfb Guidelines \u5fc5\u8bfb\u6559\u6750\u5c31\u662f\u4e00\u5b9a\u8981\u8bfb\u7684\u6559\u6750 \u5b57\u5178\u7684\u610f\u601d\u662f\uff0c\u4e00\u822c\u60c5\u51b5\u4e0b\u4e0d\u7ba1\u5b83\uff0c\u4f46\u5f53\u9047\u5230\u4e86\u4e0d\u61c2\u7684\u6982\u5ff5\u7684\u65f6\u5019\uff0c\u5c31\u53bb\u5b57\u5178\u91cc\u9762\u67e5\uff08\u800c\u4e0d\u662f\u7ef4\u57fa\u767e\u79d1\uff09 \u8fdb\u9636\u4e66\u7c4d\u5148\u4e0d\u8bfb\uff0c\u5148\u8bfb\u5b8c\u5fc5\u8bfb\u4e66\u7c4d\u3002\u5fc5\u8bfb\u4e66\u7c4d\u4e00\u822c\u90fd\u662f\u8981\u524d\u524d\u540e\u540e\u53cd\u590d\u770b\u8fc7 N \u904d\u624d\u7b97\u8bfb\u5b8c \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u6700\u91cd\u8981\u7684\u8bfb\u6cd5\u5c31\u662f\u5bf9\u6bd4\u9605\u8bfb (contrastive-comparative reading)\uff1a\u540c\u65f6\u6253\u5f00\u4e24\u672c\u4e66\u8bb2\u540c\u4e00\u4e3b\u9898\u7684\u7ae0\u8282\uff0c\u7136\u540e\u5bf9\u6bd4\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u5c3d\u91cf\u53bb\u56de\u60f3\u4e4b\u524d\u8bfb\u8fc7\u7684\u8bba\u6587\uff0c\u6bd4\u8f83\u8bba\u6587\u548c\u6559\u6750\u7684\u76f8\u540c\u70b9\u4e0e\u4e0d\u540c\u70b9 \u57fa\u7840\u8def\u5f84 \u5148\u8bfb AoS \u7b2c\u516d\u7ae0: Models, Statistical Inference and Learning\uff0c\u8fd9\u4e00\u90e8\u5206\u662f\u6700\u57fa\u7840\u7684\u79d1\u666e \u7136\u540e\u8bfb PRML \u7b2c 10, 11 \u7ae0 \u7b2c 10 \u7ae0\u7684\u5185\u5bb9\u662f Variational Inference, \u7b2c 11 \u7ae0\u7684\u5185\u5bb9\u662f MCMC, \u8fd9\u4e24\u79cd\u65b9\u6cd5\u662f\u8d1d\u53f6\u65af\u63a8\u65ad\u7684\u4e24\u6761\u6700\u4e3b\u8981\u8def\u7ebf \u5982\u679c\u5728\u8bfb PRML \u7684\u8fc7\u7a0b\u4e2d\u53d1\u73b0\u6709\u4efb\u4f55\u4e0d\u61c2\u7684\u540d\u8bcd\uff0c\u5c31\u53bb\u7ffb\u524d\u9762\u7684\u7ae0\u8282\u3002\u5f88\u5927\u6982\u7387\u80fd\u591f\u5728\u7b2c 3\uff0c4 \u7ae0\u627e\u5230\u76f8\u5bf9\u5e94\u7684\u5b9a\u4e49\uff1b\u5982\u679c\u627e\u4e0d\u5230\u6216\u8005\u4e0d\u591f\u8be6\u7ec6\uff0c\u5c31\u53bb\u67e5 MLAPP AoS \u7b2c 8 \u7ae0 (Parametric Inference) \u548c\u7b2c 11 \u7ae0 (Bayesian Inference) \u4e5f\u53ef\u4ee5\u4f5c\u4e3a\u53c2\u8003\u3002\u6700\u597d\u7684\u65b9\u6cd5\u662f\u591a\u672c\u4e66\u5bf9\u6bd4\u9605\u8bfb\uff0c\u6d41\u7a0b\u5982\u4e0b \u5047\u8bbe\u6211\u5728\u8bfb PRML \u7b2c 10 \u7ae0\u7684\u65f6\u5019\u53d1\u73b0\u4e86\u4e00\u4e2a\u4e0d\u61c2\u7684\u8bcd\uff1aposterior inference \u4e8e\u662f\u6211\u5f80\u524d\u7ffb\uff0c\u7ffb\u5230\u4e86\u7b2c 3 \u7ae0 (Linear Model for Regression)\uff0c\u770b\u5230\u4e86\u6700\u7b80\u5355\u7684 posterior \u7136\u540e\u6211\u63a5\u7740\u7ffb AoS\uff0c\u7ffb\u5230\u4e86\u7b2c 11 \u7ae0\uff0c\u4e5f\u6709\u5bf9 posterior \u7684\u63cf\u8ff0 \u7136\u540e\u6211\u5bf9\u6bd4 PRML \u7b2c 10 \u7ae0\uff0c\u7b2c 3 \u7ae0\uff0cAoS \u7b2c 11 \u7ae0\uff0c\u4e09\u5904\u4e0d\u540c\u5730\u65b9\u5bf9 posterior \u7684\u89e3\u8bfb\uff0c\u6bd4\u8f83\u5176\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u5b8c PRML \u7b2c 10 \u548c 11 \u7ae0\u4e4b\u540e\uff0c\u63a5\u7740\u8bfb AoS \u7b2c 24 \u7ae0 (Simulation Methods)\uff0c\u7136\u540e\u628a\u5b83\u548c PRML \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb -- \u8fd9\u4fe9\u90fd\u662f\u8bb2 MCMC \u5982\u679c\u5230\u6b64\u5904\u53d1\u73b0\u8fd8\u6709\u57fa\u7840\u6982\u5ff5\u8bfb\u4e0d\u61c2\uff0c\u5c31\u56de\u5230 PRML \u7b2c 3 \u7ae0\uff0c\u628a\u5b83\u548c AoS \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb Again\uff0c\u5bf9\u6bd4\u9605\u8bfb\u975e\u5e38\u91cd\u8981\uff0c\u4e00\u5b9a\u8981\u628a\u4e0d\u540c\u672c\u4e66\u7684\u7c7b\u4f3c\u5185\u5bb9\u540c\u65f6\u6446\u5728\u9762\u524d\u76f8\u4e92\u5bf9\u6bd4\uff0c\u8fd9\u6837\u53ef\u4ee5\u663e\u8457\u589e\u5f3a\u8bb0\u5fc6 \u7136\u540e\u8bfb PRML \u7b2c 13 \u7ae0\uff08\u8df3\u8fc7\u7b2c 12 \u7ae0\uff09\uff0c\u8fd9\u4e00\u7ae0\u53ef\u4ee5\u548c MLAPP \u7684\u7b2c 17, 18 \u7ae0\u5bf9\u6bd4\u9605\u8bfb MLAPP \u7b2c 17 \u7ae0\u662f PRML \u7b2c 13.2 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 HMM MLAPP \u7b2c 18 \u7ae0\u662f PRML \u7b2c 13.3 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 LDS \u8bfb\u5b8c PRML \u7b2c 13 \u7ae0\u4e4b\u540e\uff0c\u518d\u53bb\u8bfb PRML \u7b2c 8 \u7ae0 (Graphical Models) -- \u6b64\u65f6\u8fd9\u90e8\u5206\u5e94\u8be5\u4f1a\u8bfb\u5f97\u5f88\u8f7b\u677e \u4ee5\u4e0a\u7684\u5185\u5bb9\u53ef\u4ee5\u8fdb\u4e00\u6b65\u5bf9\u7167 CMU 10-708 PGM \u8bfe\u7a0b\u6750\u6599 \u5230\u76ee\u524d\u4e3a\u6b62\uff0c\u5e94\u8be5\u80fd\u591f\u638c\u63e1 \u6982\u7387\u6a21\u578b\u7684\u57fa\u7840\u5b9a\u4e49 \u7cbe\u51c6\u63a8\u65ad - Sum-Product \u8fd1\u4f3c\u63a8\u65ad - MCMC \u8fd1\u4f3c\u63a8\u65ad - VI \u7136\u540e\u5c31\u53ef\u4ee5\u53bb\u505a\u66f4\u8fdb\u9636\u7684\u5185\u5bb9","title":"\u8fdb\u9636\u8def\u7ebf\u56fe"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_1","text":"\u6b64\u8def\u7ebf\u56fe\u9002\u7528\u4e8e\u5df2\u7ecf\u5b66\u8fc7\u4e86\u57fa\u7840\u673a\u5668\u5b66\u4e60 (ML, NLP, CV, RL) \u7684\u540c\u5b66 (\u9ad8\u5e74\u7ea7\u672c\u79d1\u751f\u6216\u4f4e\u5e74\u7ea7\u7814\u7a76\u751f)\uff0c\u5df2\u7ecf\u53d1\u8868\u8fc7\u81f3\u5c11\u4e00\u7bc7\u9876\u4f1a\u8bba\u6587 (NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, CVPR, ICCV) \u60f3\u8981\u8d70\u673a\u5668\u5b66\u4e60\u79d1\u7814\u8def\u7ebf\u7684\u9009\u624b\u3002 \u6b64\u8def\u7ebf\u7684\u76ee\u6807\u662f\u4e3a\u8bfb\u61c2\u4e0e\u53d1\u8868\u673a\u5668\u5b66\u4e60\u9876\u4f1a\u8bba\u6587\u6253\u4e0b\u7406\u8bba\u57fa\u7840\uff0c\u7279\u522b\u662f Probabilistic Methods \u8fd9\u4e2a track \u4e0b\u7684\u6587\u7ae0 \u673a\u5668\u5b66\u4e60\u8fdb\u9636\u53ef\u80fd\u5b58\u5728\u591a\u79cd\u4e0d\u540c\u7684\u5b66\u4e60\u8def\u7ebf\uff0c\u6b64\u8def\u7ebf\u53ea\u80fd\u4ee3\u8868\u4f5c\u8005 Yao Fu \u6240\u7406\u89e3\u7684\u6700\u4f73\u8def\u5f84\uff0c\u4fa7\u91cd\u4e8e\u8d1d\u53f6\u65af\u5b66\u6d3e\u4e0b\u7684\u6982\u7387\u5efa\u6a21\u65b9\u6cd5\uff0c\u4e5f\u4f1a\u6d89\u53ca\u5230\u5404\u9879\u76f8\u5173\u5b66\u79d1\u7684\u4ea4\u53c9\u77e5\u8bc6\u3002","title":"\u673a\u5668\u5b66\u4e60\u8fdb\u9636"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_2","text":"PRML: Pattern Recognition and Machine Learning. Christopher Bishop \u7ecf\u5178\u8d1d\u53f6\u65af\u5b66\u6d3e\u6559\u6750 AoS: All of Statistics. Larry Wasserman \u7ecf\u5178\u9891\u7387\u5b66\u6d3e\u6559\u6750 \u6240\u4ee5\u8fd9\u4e24\u672c\u4e66\u521a\u597d\u76f8\u8f85\u76f8\u6210","title":"\u5fc5\u8bfb\u6559\u6750"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_3","text":"MLAPP: Machine Learning: A Probabilistic Perspective. Kevin Murphy Convex Optimization. Stephen Boyd and Lieven Vandenberghe","title":"\u5b57\u5178"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_4","text":"W&J: Graphical Models, Exponential Families, and Variational Inference. Martin Wainwright and Michael Jordan Theory of Point Estimation. E. L. Lehmann and George Casella","title":"\u8fdb\u9636\u4e66\u7c4d"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_5","text":"","title":"\u5982\u4f55\u9605\u8bfb"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#guidelines","text":"\u5fc5\u8bfb\u6559\u6750\u5c31\u662f\u4e00\u5b9a\u8981\u8bfb\u7684\u6559\u6750 \u5b57\u5178\u7684\u610f\u601d\u662f\uff0c\u4e00\u822c\u60c5\u51b5\u4e0b\u4e0d\u7ba1\u5b83\uff0c\u4f46\u5f53\u9047\u5230\u4e86\u4e0d\u61c2\u7684\u6982\u5ff5\u7684\u65f6\u5019\uff0c\u5c31\u53bb\u5b57\u5178\u91cc\u9762\u67e5\uff08\u800c\u4e0d\u662f\u7ef4\u57fa\u767e\u79d1\uff09 \u8fdb\u9636\u4e66\u7c4d\u5148\u4e0d\u8bfb\uff0c\u5148\u8bfb\u5b8c\u5fc5\u8bfb\u4e66\u7c4d\u3002\u5fc5\u8bfb\u4e66\u7c4d\u4e00\u822c\u90fd\u662f\u8981\u524d\u524d\u540e\u540e\u53cd\u590d\u770b\u8fc7 N \u904d\u624d\u7b97\u8bfb\u5b8c \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u6700\u91cd\u8981\u7684\u8bfb\u6cd5\u5c31\u662f\u5bf9\u6bd4\u9605\u8bfb (contrastive-comparative reading)\uff1a\u540c\u65f6\u6253\u5f00\u4e24\u672c\u4e66\u8bb2\u540c\u4e00\u4e3b\u9898\u7684\u7ae0\u8282\uff0c\u7136\u540e\u5bf9\u6bd4\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u5c3d\u91cf\u53bb\u56de\u60f3\u4e4b\u524d\u8bfb\u8fc7\u7684\u8bba\u6587\uff0c\u6bd4\u8f83\u8bba\u6587\u548c\u6559\u6750\u7684\u76f8\u540c\u70b9\u4e0e\u4e0d\u540c\u70b9","title":"Guidelines"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_6","text":"\u5148\u8bfb AoS \u7b2c\u516d\u7ae0: Models, Statistical Inference and Learning\uff0c\u8fd9\u4e00\u90e8\u5206\u662f\u6700\u57fa\u7840\u7684\u79d1\u666e \u7136\u540e\u8bfb PRML \u7b2c 10, 11 \u7ae0 \u7b2c 10 \u7ae0\u7684\u5185\u5bb9\u662f Variational Inference, \u7b2c 11 \u7ae0\u7684\u5185\u5bb9\u662f MCMC, \u8fd9\u4e24\u79cd\u65b9\u6cd5\u662f\u8d1d\u53f6\u65af\u63a8\u65ad\u7684\u4e24\u6761\u6700\u4e3b\u8981\u8def\u7ebf \u5982\u679c\u5728\u8bfb PRML \u7684\u8fc7\u7a0b\u4e2d\u53d1\u73b0\u6709\u4efb\u4f55\u4e0d\u61c2\u7684\u540d\u8bcd\uff0c\u5c31\u53bb\u7ffb\u524d\u9762\u7684\u7ae0\u8282\u3002\u5f88\u5927\u6982\u7387\u80fd\u591f\u5728\u7b2c 3\uff0c4 \u7ae0\u627e\u5230\u76f8\u5bf9\u5e94\u7684\u5b9a\u4e49\uff1b\u5982\u679c\u627e\u4e0d\u5230\u6216\u8005\u4e0d\u591f\u8be6\u7ec6\uff0c\u5c31\u53bb\u67e5 MLAPP AoS \u7b2c 8 \u7ae0 (Parametric Inference) \u548c\u7b2c 11 \u7ae0 (Bayesian Inference) \u4e5f\u53ef\u4ee5\u4f5c\u4e3a\u53c2\u8003\u3002\u6700\u597d\u7684\u65b9\u6cd5\u662f\u591a\u672c\u4e66\u5bf9\u6bd4\u9605\u8bfb\uff0c\u6d41\u7a0b\u5982\u4e0b \u5047\u8bbe\u6211\u5728\u8bfb PRML \u7b2c 10 \u7ae0\u7684\u65f6\u5019\u53d1\u73b0\u4e86\u4e00\u4e2a\u4e0d\u61c2\u7684\u8bcd\uff1aposterior inference \u4e8e\u662f\u6211\u5f80\u524d\u7ffb\uff0c\u7ffb\u5230\u4e86\u7b2c 3 \u7ae0 (Linear Model for Regression)\uff0c\u770b\u5230\u4e86\u6700\u7b80\u5355\u7684 posterior \u7136\u540e\u6211\u63a5\u7740\u7ffb AoS\uff0c\u7ffb\u5230\u4e86\u7b2c 11 \u7ae0\uff0c\u4e5f\u6709\u5bf9 posterior \u7684\u63cf\u8ff0 \u7136\u540e\u6211\u5bf9\u6bd4 PRML \u7b2c 10 \u7ae0\uff0c\u7b2c 3 \u7ae0\uff0cAoS \u7b2c 11 \u7ae0\uff0c\u4e09\u5904\u4e0d\u540c\u5730\u65b9\u5bf9 posterior \u7684\u89e3\u8bfb\uff0c\u6bd4\u8f83\u5176\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u5b8c PRML \u7b2c 10 \u548c 11 \u7ae0\u4e4b\u540e\uff0c\u63a5\u7740\u8bfb AoS \u7b2c 24 \u7ae0 (Simulation Methods)\uff0c\u7136\u540e\u628a\u5b83\u548c PRML \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb -- \u8fd9\u4fe9\u90fd\u662f\u8bb2 MCMC \u5982\u679c\u5230\u6b64\u5904\u53d1\u73b0\u8fd8\u6709\u57fa\u7840\u6982\u5ff5\u8bfb\u4e0d\u61c2\uff0c\u5c31\u56de\u5230 PRML \u7b2c 3 \u7ae0\uff0c\u628a\u5b83\u548c AoS \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb Again\uff0c\u5bf9\u6bd4\u9605\u8bfb\u975e\u5e38\u91cd\u8981\uff0c\u4e00\u5b9a\u8981\u628a\u4e0d\u540c\u672c\u4e66\u7684\u7c7b\u4f3c\u5185\u5bb9\u540c\u65f6\u6446\u5728\u9762\u524d\u76f8\u4e92\u5bf9\u6bd4\uff0c\u8fd9\u6837\u53ef\u4ee5\u663e\u8457\u589e\u5f3a\u8bb0\u5fc6 \u7136\u540e\u8bfb PRML \u7b2c 13 \u7ae0\uff08\u8df3\u8fc7\u7b2c 12 \u7ae0\uff09\uff0c\u8fd9\u4e00\u7ae0\u53ef\u4ee5\u548c MLAPP \u7684\u7b2c 17, 18 \u7ae0\u5bf9\u6bd4\u9605\u8bfb MLAPP \u7b2c 17 \u7ae0\u662f PRML \u7b2c 13.2 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 HMM MLAPP \u7b2c 18 \u7ae0\u662f PRML \u7b2c 13.3 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 LDS \u8bfb\u5b8c PRML \u7b2c 13 \u7ae0\u4e4b\u540e\uff0c\u518d\u53bb\u8bfb PRML \u7b2c 8 \u7ae0 (Graphical Models) -- \u6b64\u65f6\u8fd9\u90e8\u5206\u5e94\u8be5\u4f1a\u8bfb\u5f97\u5f88\u8f7b\u677e \u4ee5\u4e0a\u7684\u5185\u5bb9\u53ef\u4ee5\u8fdb\u4e00\u6b65\u5bf9\u7167 CMU 10-708 PGM \u8bfe\u7a0b\u6750\u6599 \u5230\u76ee\u524d\u4e3a\u6b62\uff0c\u5e94\u8be5\u80fd\u591f\u638c\u63e1 \u6982\u7387\u6a21\u578b\u7684\u57fa\u7840\u5b9a\u4e49 \u7cbe\u51c6\u63a8\u65ad - Sum-Product \u8fd1\u4f3c\u63a8\u65ad - MCMC \u8fd1\u4f3c\u63a8\u65ad - VI \u7136\u540e\u5c31\u53ef\u4ee5\u53bb\u505a\u66f4\u8fdb\u9636\u7684\u5185\u5bb9","title":"\u57fa\u7840\u8def\u5f84"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/","text":"CS224n: Natural Language Processing \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684 NLP \u5165\u95e8\u8bfe\u7a0b\uff0c\u7531\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9886\u57df\u7684\u5de8\u4f6c Chris Manning \u9886\u8854\u6559\u6388\uff08word2vec \u7b97\u6cd5\u7684\u5f00\u521b\u8005\uff09\u3002\u5185\u5bb9\u8986\u76d6\u4e86\u8bcd\u5411\u91cf\u3001RNN\u3001LSTM\u3001Seq2Seq \u6a21\u578b\u3001\u673a\u5668\u7ffb\u8bd1\u3001\u6ce8\u610f\u529b\u673a\u5236\u3001Transformer \u7b49\u7b49 NLP \u9886\u57df\u7684\u6838\u5fc3\u77e5\u8bc6\u70b9\u3002 5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\u96be\u5ea6\u5faa\u5e8f\u6e10\u8fdb\uff0c\u5206\u522b\u662f\u8bcd\u5411\u91cf\u3001word2vec \u7b97\u6cd5\u3001Dependency parsing\u3001\u673a\u5668\u7ffb\u8bd1\u4ee5\u53ca Transformer \u7684 fine-tune\u3002 \u6700\u7ec8\u7684\u5927\u4f5c\u4e1a\u662f\u5728 Stanford \u8457\u540d\u7684 SQuAD \u6570\u636e\u96c6\u4e0a\u8bad\u7ec3 QA \u6a21\u578b\uff0c\u6709\u5b66\u751f\u7684\u5927\u4f5c\u4e1a\u751a\u81f3\u76f4\u63a5\u53d1\u8868\u4e86\u9876\u4f1a\u8bba\u6587\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs224n/index.html \u8bfe\u7a0b\u89c6\u9891\uff1aB \u7ad9\u641c\u7d22 CS224n \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224n/index.html \uff0c5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a + 1 \u4e2a Final Project \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS224n - GitHub \u4e2d\u3002","title":"Stanford CS224n: Natural Language Processing"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#cs224n-natural-language-processing","text":"","title":"CS224n: Natural Language Processing"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684 NLP \u5165\u95e8\u8bfe\u7a0b\uff0c\u7531\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9886\u57df\u7684\u5de8\u4f6c Chris Manning \u9886\u8854\u6559\u6388\uff08word2vec \u7b97\u6cd5\u7684\u5f00\u521b\u8005\uff09\u3002\u5185\u5bb9\u8986\u76d6\u4e86\u8bcd\u5411\u91cf\u3001RNN\u3001LSTM\u3001Seq2Seq \u6a21\u578b\u3001\u673a\u5668\u7ffb\u8bd1\u3001\u6ce8\u610f\u529b\u673a\u5236\u3001Transformer \u7b49\u7b49 NLP \u9886\u57df\u7684\u6838\u5fc3\u77e5\u8bc6\u70b9\u3002 5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\u96be\u5ea6\u5faa\u5e8f\u6e10\u8fdb\uff0c\u5206\u522b\u662f\u8bcd\u5411\u91cf\u3001word2vec \u7b97\u6cd5\u3001Dependency parsing\u3001\u673a\u5668\u7ffb\u8bd1\u4ee5\u53ca Transformer \u7684 fine-tune\u3002 \u6700\u7ec8\u7684\u5927\u4f5c\u4e1a\u662f\u5728 Stanford \u8457\u540d\u7684 SQuAD \u6570\u636e\u96c6\u4e0a\u8bad\u7ec3 QA \u6a21\u578b\uff0c\u6709\u5b66\u751f\u7684\u5927\u4f5c\u4e1a\u751a\u81f3\u76f4\u63a5\u53d1\u8868\u4e86\u9876\u4f1a\u8bba\u6587\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs224n/index.html \u8bfe\u7a0b\u89c6\u9891\uff1aB \u7ad9\u641c\u7d22 CS224n \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224n/index.html \uff0c5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a + 1 \u4e2a Final Project","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS224n - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/","text":"CS224w: Machine Learning with Graphs \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython, LaTeX \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684\u56fe\u795e\u7ecf\u7f51\u7edc\u5165\u95e8\u8bfe\uff0c\u8fd9\u95e8\u8bfe\u6211\u6ca1\u6709\u4e0a\u8fc7\uff0c\u4f46\u4f17\u591a\u505a GNN \u7684\u670b\u53cb\u90fd\u5411\u6211\u529b\u8350\u8fc7\u8fd9\u95e8\u8bfe\uff0c\u60f3\u5fc5 Stanford \u7684\u8bfe\u8d28\u91cf\u8fd8\u662f\u4e00\u5982\u65e2\u5f80\u5730\u6709\u4fdd\u8bc1\u7684\u3002\u53e6\u5916\u5c31\u662f\u8fd9\u95e8\u8bfe\u7684\u6388\u8bfe\u8001\u5e08\u975e\u5e38\u5e74\u8f7b\u5e05\u6c14:) \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs224w/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/watch?v=JAB_plj2rbA \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224w/ \uff0c6 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\uff0c3 \u4e2a LaTeX \u4e66\u9762\u4f5c\u4e1a","title":"Stanford CS224w: Machine Learning with Graphs"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/#cs224w-machine-learning-with-graphs","text":"","title":"CS224w: Machine Learning with Graphs"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython, LaTeX \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684\u56fe\u795e\u7ecf\u7f51\u7edc\u5165\u95e8\u8bfe\uff0c\u8fd9\u95e8\u8bfe\u6211\u6ca1\u6709\u4e0a\u8fc7\uff0c\u4f46\u4f17\u591a\u505a GNN \u7684\u670b\u53cb\u90fd\u5411\u6211\u529b\u8350\u8fc7\u8fd9\u95e8\u8bfe\uff0c\u60f3\u5fc5 Stanford \u7684\u8bfe\u8d28\u91cf\u8fd8\u662f\u4e00\u5982\u65e2\u5f80\u5730\u6709\u4fdd\u8bc1\u7684\u3002\u53e6\u5916\u5c31\u662f\u8fd9\u95e8\u8bfe\u7684\u6388\u8bfe\u8001\u5e08\u975e\u5e38\u5e74\u8f7b\u5e05\u6c14:)","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs224w/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/watch?v=JAB_plj2rbA \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224w/ \uff0c6 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\uff0c3 \u4e2a LaTeX \u4e66\u9762\u4f5c\u4e1a","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS230/","text":"Coursera: Deep Learning \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u673a\u5668\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 \u5434\u6069\u8fbe\u5728 Coursera \u5f00\u8bbe\u7684\u53e6\u4e00\u95e8\u7f51\u7ea2\u8bfe\u7a0b\uff0c\u5b66\u4e60\u8005\u65e0\u6570\uff0c\u582a\u79f0\u5723\u7ecf\u7ea7\u7684\u6df1\u5ea6\u5b66\u4e60\u5165\u95e8\u8bfe\u3002\u6df1\u5165\u6d45\u51fa\u7684\u8bb2\u89e3\uff0c\u773c\u82b1\u7f2d\u4e71\u7684 Project\u3002\u4ece\u6700\u57fa\u7840\u7684\u795e\u7ecf\u7f51\u7edc\uff0c\u5230 CNN, RNN\uff0c\u518d\u5230\u6700\u8fd1\u5927\u70ed\u7684 Transformer\u3002\u5b66\u5b8c\u8fd9\u95e8\u8bfe\uff0c\u4f60\u5c06\u521d\u6b65\u638c\u63e1\u6df1\u5ea6\u5b66\u4e60\u9886\u57df\u5fc5\u5907\u7684\u77e5\u8bc6\u548c\u6280\u80fd\uff0c\u5e76\u4e14\u53ef\u4ee5\u5728 Kaggle \u4e2d\u53c2\u52a0\u81ea\u5df1\u611f\u5174\u8da3\u7684\u6bd4\u8d5b\uff0c\u5728\u5b9e\u8df5\u4e2d\u953b\u70bc\u81ea\u5df1\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.coursera.org/specializations/deep-learning \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.coursera.org/specializations/deep-learning \uff0cB\u7ad9\u6709\u642c\u8fd0 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://www.coursera.org/specializations/deep-learning","title":"Coursera: Deep Learning"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS230/#coursera-deep-learning","text":"","title":"Coursera: Deep Learning"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS230/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u673a\u5668\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 \u5434\u6069\u8fbe\u5728 Coursera 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https://www.coursera.org/specializations/deep-learning \uff0cB\u7ad9\u6709\u642c\u8fd0 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://www.coursera.org/specializations/deep-learning","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS231/","text":"CS231n: CNN for Visual Recognition \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u673a\u5668\u5b66\u4e60\u57fa\u7840 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684 CV \u5165\u95e8\u8bfe\uff0c\u7531\u8ba1\u7b97\u673a\u9886\u57df\u7684\u5de8\u4f6c\u674e\u98de\u98de\u9662\u58eb\u9886\u8854\u6559\u6388\uff08CV \u9886\u57df\u5212\u65f6\u4ee3\u7684\u8457\u540d\u6570\u636e\u96c6 ImageNet 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Regression\u3001Classification\u3001CNN\u3001Self-Attention\u3001Transformer\u3001GAN\u3001BERT\u3001Anomaly Detection\u3001Explainable AI\u3001Attack\u3001Adaptation\u3001 RL\u3001Compression\u3001Life-Long Learning \u4ee5\u53ca Meta Learning\u3002\u53ef\u8c13\u662f\u5305\u7f57\u4e07\u8c61\uff0c\u80fd\u8ba9\u5b66\u751f\u5bf9\u4e8e\u6df1\u5ea6\u5b66\u4e60\u7684\u7edd\u5927\u591a\u6570\u9886\u57df\u90fd\u6709\u4e00\u5b9a\u4e86\u89e3\uff0c\u4ece\u800c\u53ef\u4ee5\u8fdb\u4e00\u6b65\u9009\u62e9\u60f3\u8981\u6df1\u5165\u7684\u65b9\u5411\u8fdb\u884c\u5b66\u4e60\u3002 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Oppenheim \u597d\u7684\uff0c\u4e0a\u8fd9\u95e8\u8bfe\u7684\u7406\u7531\u5df2\u7ecf\u8db3\u591f\u4e86\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/Signals_and_Systems_AVO/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://ocw.mit.edu/resources/res-6-007-signals-and-systems-spring-2011/index.htm \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1CZ4y1j7hs \u8bfe\u7a0b\u6559\u6750\uff1aSignals and Systems, 2nd Edition \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://ocw.mit.edu/resources/res-6-007-signals-and-systems-spring-2011/assignments","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/signal/","text":"UCB EE120: Signal and Systems \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A, CS70\uff0c\u5fae\u79ef\u5206\uff0c\u7ebf\u6027\u4ee3\u6570 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u6700\u7cbe\u534e\u7684\u90e8\u5206\u5c31\u662f 6 \u4e2a\u8d85\u6709\u8da3\u7684\u7f16\u7a0b\u4f5c\u4e1a\u4e86\uff0c\u4f1a\u8ba9\u4f60\u7528 Python \u901a\u8fc7\u5b66\u4e60\u5230\u7684\u4fe1\u53f7\u4e0e\u7cfb\u7edf\u7684\u7406\u8bba\u77e5\u8bc6\uff0c\u89e3\u51b3\u5404\u7c7b\u5b9e\u9645\u95ee\u9898\u3002\u4f8b\u5982 lab3 \u4f1a\u8ba9\u4f60\u5b9e\u73b0 FFT \u7b97\u6cd5\uff0c\u5e76\u548c Numpy \u7684\u5b98\u65b9\u5b9e\u73b0\u8fdb\u884c\u6027\u80fd\u5bf9\u6bd4\uff1blab4 \u4f1a\u901a\u8fc7\u5206\u6790\u624b\u6307\u5934\u7684\u5f71\u50cf\u6570\u636e\u63a8\u65ad\u5fc3\u7387\uff1blab5 \u5c31\u66f4\u725b\u4e86\uff0c\u4f1a\u8ba9\u4f60\u7ed9\u54c8\u52c3\u671b\u8fdc\u955c\u62cd\u5230\u7684\u7167\u7247\u8fdb\u884c\u964d\u566a\u5904\u7406\uff0c\u6062\u590d\u7eda\u70c2\u6e05\u6670\u7684\u661f\u7a7a\uff1blab6 \u4f1a\u8ba9\u4f60\u6784\u9020\u4e00\u4e2a\u53cd\u9988\u7cfb\u7edf\uff0c\u5e73\u8861\u5c0f\u8f66\u4e0a\u7684\u7ec6\u6746\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://inst.eecs.berkeley.edu/~ee120/fa19/ \u8bfe\u7a0b\u6559\u6750\uff1a\u53c2\u89c1\u8bfe\u7a0b notes \u8bfe\u7a0b\u4f5c\u4e1a\uff1a5 \u4e2a\u4e66\u9762\u4f5c\u4e1a + 6 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/UCB-EE120 - 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GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"%E7%A8%8B%E5%BA%8F%E8%AF%AD%E8%A8%80%E8%AE%BE%E8%AE%A1/CS242/","text":"","title":"CS242"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/","text":"UCB CS161: Computer Security \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A, CS61B, CS61C \u7f16\u7a0b\u8bed\u8a00\uff1aC, Go \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 \u4f2f\u514b\u5229\u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u8bfe\u7a0b\u5185\u5bb9\u5206\u4e3a5\u4e2a\u90e8\u5206\uff1a Security principles: how to design a secure system Memory safety: buffer overflow attack Cryptography: symmetric encryption, asymmetric encryption, MAC, digital signature ......... Web: SQL-injection, XSS, XSRF ....... Networking: attacks for each layer \u8fd9\u95e8\u8bfe\u8ba9\u6211\u5370\u8c61\u6700\u4e3a\u6df1\u523b\u7684\u90e8\u5206\u662f Project2\uff0c\u8ba9\u4f60\u7528 Go \u8bed\u8a00\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7684\u6587\u4ef6\u5206\u4eab\u7cfb\u7edf\u3002\u6211\u82b1\u4e86\u6574\u6574\u4e09\u5929\u624d\u5b8c\u6210\u4e86\u8fd9\u4e2a\u975e\u5e38\u8650\u7684 Project\uff0c\u603b\u4ee3\u7801\u91cf\u8d85\u8fc7 3k \u884c\u3002\u5728\u8fd9\u6837\u5bc6\u96c6\u578b\u7684\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u80fd\u6781\u5927\u5730\u953b\u70bc\u4f60\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7cfb\u7edf\u7684\u80fd\u529b\u3002 2020 \u5e74\u590f\u5b63\u5b66\u671f\u7684\u7248\u672c\u5f00\u6e90\u4e86\u8bfe\u7a0b\u5f55\u5f71\uff0c\u5927\u5bb6\u53ef\u4ee5\u5728\u4e0b\u9762\u7684\u8bfe\u7a0b\u7f51\u7ad9\u94fe\u63a5\u91cc\u627e\u5230\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://su20.cs161.org/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a https://textbook.cs161.org/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a7 \u4e2a\u5728\u7ebf HW + 3 \u4e2a Lab + 3 \u4e2a Project \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/UCB-CS161 - GitHub \u4e2d\u3002","title":"UCB CS161: Computer Security"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#ucb-cs161-computer-security","text":"","title":"UCB CS161: Computer Security"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A, CS61B, CS61C \u7f16\u7a0b\u8bed\u8a00\uff1aC, Go \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 \u4f2f\u514b\u5229\u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u8bfe\u7a0b\u5185\u5bb9\u5206\u4e3a5\u4e2a\u90e8\u5206\uff1a Security principles: how to design a secure system Memory safety: buffer overflow attack Cryptography: symmetric encryption, asymmetric encryption, MAC, digital signature ......... Web: SQL-injection, XSS, XSRF ....... Networking: attacks for each layer \u8fd9\u95e8\u8bfe\u8ba9\u6211\u5370\u8c61\u6700\u4e3a\u6df1\u523b\u7684\u90e8\u5206\u662f Project2\uff0c\u8ba9\u4f60\u7528 Go \u8bed\u8a00\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7684\u6587\u4ef6\u5206\u4eab\u7cfb\u7edf\u3002\u6211\u82b1\u4e86\u6574\u6574\u4e09\u5929\u624d\u5b8c\u6210\u4e86\u8fd9\u4e2a\u975e\u5e38\u8650\u7684 Project\uff0c\u603b\u4ee3\u7801\u91cf\u8d85\u8fc7 3k \u884c\u3002\u5728\u8fd9\u6837\u5bc6\u96c6\u578b\u7684\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u80fd\u6781\u5927\u5730\u953b\u70bc\u4f60\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7cfb\u7edf\u7684\u80fd\u529b\u3002 2020 \u5e74\u590f\u5b63\u5b66\u671f\u7684\u7248\u672c\u5f00\u6e90\u4e86\u8bfe\u7a0b\u5f55\u5f71\uff0c\u5927\u5bb6\u53ef\u4ee5\u5728\u4e0b\u9762\u7684\u8bfe\u7a0b\u7f51\u7ad9\u94fe\u63a5\u91cc\u627e\u5230\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://su20.cs161.org/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a https://textbook.cs161.org/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a7 \u4e2a\u5728\u7ebf HW + 3 \u4e2a Lab + 3 \u4e2a Project","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/UCB-CS161 - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/","text":"MIT 6.858: Computer System Security \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\uff0c\u5bf9\u8ba1\u7b97\u673a\u7cfb\u7edf\u6709\u521d\u6b65\u4e86\u89e3 \u7f16\u7a0b\u8bed\u8a00\uff1aC, Python \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 MIT \u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u5b9e\u9a8c\u73af\u5883\u662f\u4e00\u4e2a Web Application Zoobar\u3002\u5b66\u751f\u5b66\u4e60\u653b\u9632\u6280\u672f\u5e76\u5e94\u7528\u4e8e\u8be5 Web Application\u3002 Lab 1: you will explore the zoobar web application, and use buffer overflow attacks to break its security properties. Lab 2: you will improve the zoobar web application by using privilege separation, so that if one component is compromised, the adversary doesn't get control over the whole web application. Lab 3: you will build a program analysis tool based on symbolic execution to find bugs in Python code such as the zoobar web application. Lab 4: you will improve the zoobar application against browser attacks. \u8fd9\u4e2a\u8bfe\u6211\u4e3b\u8981\u662f\u505a\u4e86 Lab 3\u3002Lab 3 \u662f\u901a\u8fc7\u6df7\u5408\u7b26\u53f7\u6267\u884c\u6765\u904d\u5386\u7a0b\u5e8f\u7684\u6240\u6709\u5206\u652f\uff0c\u7406\u89e3\u4e86\u7b26\u53f7\u6267\u884c\u7684\u601d\u60f3\u540e Lab \u5e76\u4e0d\u96be\u505a\u3002\u8fd9\u4e2a Lab \u76f4\u89c2\u5c55\u793a\u7b26\u53f7\u6267\u884c\u8fd9\u79cd\u6280\u672f\u7684\u4f7f\u7528\u65b9\u6cd5\u3002 \u8fd9\u4e2a\u8bfe\u7684 Final Project \u662f\u5b9e\u73b0 SecFS \uff0c\u4e00\u4e2a\u8fdc\u7aef\u6587\u4ef6\u7cfb\u7edf\uff0c\u9762\u5bf9\u5b8c\u5168\u4e0d\u53ef\u4fe1\u7684\u670d\u52a1\u5668\uff0c\u63d0\u4f9b\u673a\u5bc6\u6027\u548c\u5b8c\u6574\u6027\u3002\u53c2\u8003\u8bba\u6587\u4e3a SUNDR \u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://css.csail.mit.edu/6.858/2022/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a4 \u4e2a Lab + Final Project / Lab5","title":"MIT 6.858: Computer System Security"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/#mit-6858-computer-system-security","text":"","title":"MIT 6.858: Computer System Security"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\uff0c\u5bf9\u8ba1\u7b97\u673a\u7cfb\u7edf\u6709\u521d\u6b65\u4e86\u89e3 \u7f16\u7a0b\u8bed\u8a00\uff1aC, Python \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 MIT \u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u5b9e\u9a8c\u73af\u5883\u662f\u4e00\u4e2a Web Application Zoobar\u3002\u5b66\u751f\u5b66\u4e60\u653b\u9632\u6280\u672f\u5e76\u5e94\u7528\u4e8e\u8be5 Web Application\u3002 Lab 1: you will explore the zoobar web application, and use buffer overflow attacks to break its security properties. Lab 2: you will improve the zoobar web application by using privilege separation, so that if one component is compromised, the adversary doesn't get control over the whole web application. Lab 3: you will build a program analysis tool based on symbolic execution to find bugs in Python code such as the zoobar web application. Lab 4: you will improve the zoobar application against browser attacks. \u8fd9\u4e2a\u8bfe\u6211\u4e3b\u8981\u662f\u505a\u4e86 Lab 3\u3002Lab 3 \u662f\u901a\u8fc7\u6df7\u5408\u7b26\u53f7\u6267\u884c\u6765\u904d\u5386\u7a0b\u5e8f\u7684\u6240\u6709\u5206\u652f\uff0c\u7406\u89e3\u4e86\u7b26\u53f7\u6267\u884c\u7684\u601d\u60f3\u540e Lab \u5e76\u4e0d\u96be\u505a\u3002\u8fd9\u4e2a Lab \u76f4\u89c2\u5c55\u793a\u7b26\u53f7\u6267\u884c\u8fd9\u79cd\u6280\u672f\u7684\u4f7f\u7528\u65b9\u6cd5\u3002 \u8fd9\u4e2a\u8bfe\u7684 Final Project \u662f\u5b9e\u73b0 SecFS \uff0c\u4e00\u4e2a\u8fdc\u7aef\u6587\u4ef6\u7cfb\u7edf\uff0c\u9762\u5bf9\u5b8c\u5168\u4e0d\u53ef\u4fe1\u7684\u670d\u52a1\u5668\uff0c\u63d0\u4f9b\u673a\u5bc6\u6027\u548c\u5b8c\u6574\u6027\u3002\u53c2\u8003\u8bba\u6587\u4e3a SUNDR \u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://css.csail.mit.edu/6.858/2022/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a4 \u4e2a Lab + Final Project / Lab5","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/","text":"Stanford CS106B/X: Programming Abstractions in C++ \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u57fa\u7840 (CS50/CS106A/CS61A or equivalent) \u7f16\u7a0b\u8bed\u8a00\uff1aC++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a50-70 \u5c0f\u65f6 Stanford \u7684\u8fdb\u9636\u7f16\u7a0b\u8bfe\uff0cCS106X \u5728\u96be\u5ea6\u548c\u6df1\u5ea6\u4e0a\u4f1a\u6bd4 CS106B \u6709\u6240\u63d0\u9ad8\uff0c\u4f46\u4e3b\u4f53\u5185\u5bb9\u7c7b\u4f3c\u3002\u4e3b\u8981\u901a\u8fc7 C++ \u8bed\u8a00\u8ba9\u5b66\u751f\u5728\u5b9e\u9645\u7684\u7f16\u7a0b\u4f5c\u4e1a\u91cc\u57f9\u517b\u901a\u8fc7\u7f16\u7a0b\u62bd\u8c61\u89e3\u51b3\u5b9e\u9645\u95ee\u9898\u7684\u80fd\u529b\uff0c\u540c\u65f6\u4e5f\u4f1a\u6d89\u53ca\u4e00\u4e9b\u7b80\u5355\u7684\u6570\u636e\u7ed3\u6784\u548c\u7b97\u6cd5\u7684\u77e5\u8bc6\uff0c\u4f46\u603b\u4f53\u6765\u8bf4\u6ca1\u6709\u4e00\u95e8\u4e13\u95e8\u7684\u6570\u636e\u7ed3\u6784\u8bfe\u90a3\u4e48\u7cfb\u7edf\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a CS106B , CS106X \u8bfe\u7a0b\u6559\u6750\uff1a https://web.stanford.edu/class/cs106x/res/reader/CS106BX-Reader.pdf \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1G7411k7jG","title":"Stanford CS106B/X"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/#stanford-cs106bx-programming-abstractions-in-c","text":"","title":"Stanford CS106B/X: Programming Abstractions in C++"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u57fa\u7840 (CS50/CS106A/CS61A or equivalent) \u7f16\u7a0b\u8bed\u8a00\uff1aC++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a50-70 \u5c0f\u65f6 Stanford \u7684\u8fdb\u9636\u7f16\u7a0b\u8bfe\uff0cCS106X \u5728\u96be\u5ea6\u548c\u6df1\u5ea6\u4e0a\u4f1a\u6bd4 CS106B \u6709\u6240\u63d0\u9ad8\uff0c\u4f46\u4e3b\u4f53\u5185\u5bb9\u7c7b\u4f3c\u3002\u4e3b\u8981\u901a\u8fc7 C++ \u8bed\u8a00\u8ba9\u5b66\u751f\u5728\u5b9e\u9645\u7684\u7f16\u7a0b\u4f5c\u4e1a\u91cc\u57f9\u517b\u901a\u8fc7\u7f16\u7a0b\u62bd\u8c61\u89e3\u51b3\u5b9e\u9645\u95ee\u9898\u7684\u80fd\u529b\uff0c\u540c\u65f6\u4e5f\u4f1a\u6d89\u53ca\u4e00\u4e9b\u7b80\u5355\u7684\u6570\u636e\u7ed3\u6784\u548c\u7b97\u6cd5\u7684\u77e5\u8bc6\uff0c\u4f46\u603b\u4f53\u6765\u8bf4\u6ca1\u6709\u4e00\u95e8\u4e13\u95e8\u7684\u6570\u636e\u7ed3\u6784\u8bfe\u90a3\u4e48\u7cfb\u7edf\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a CS106B , CS106X \u8bfe\u7a0b\u6559\u6750\uff1a https://web.stanford.edu/class/cs106x/res/reader/CS106BX-Reader.pdf \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1G7411k7jG","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/","text":"CS106L: Standard C++ Programming \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6700\u597d\u638c\u63e1\u81f3\u5c11\u4e00\u95e8\u7f16\u7a0b\u8bed\u8a00 \u7f16\u7a0b\u8bed\u8a00\uff1aC++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a20 \u5c0f\u65f6 \u6211\u4ece\u5927\u4e00\u5f00\u59cb\u4e00\u76f4\u90fd\u662f\u5199\u7684 C++ \u4ee3\u7801\uff0c\u76f4\u5230\u5b66\u5b8c\u8fd9\u95e8\u8bfe\u6211\u624d\u610f\u8bc6\u5230\uff0c\u6211\u5199\u7684 C++ \u4ee3\u7801\u5927\u6982\u53ea\u662f C \u8bed\u8a00 + cin / cout \u800c\u5df2\u3002 \u8fd9\u95e8\u8bfe\u4f1a\u6df1\u5165\u5230\u5f88\u591a\u6807\u51c6 C++ \u7684\u7279\u6027\u548c\u8bed\u6cd5\uff0c\u8ba9\u4f60\u7f16\u5199\u51fa\u9ad8\u8d28\u91cf\u7684 C++ \u4ee3\u7801\u3002\u4f8b\u5982 auto binding, uniform initialization, lambda function, move semantics\uff0cRAII \u7b49\u6280\u5de7\u90fd\u5728\u6211\u6b64\u540e\u7684\u4ee3\u7801\u751f\u6daf\u4e2d\u88ab\u53cd\u590d\u7528\u5230\uff0c\u975e\u5e38\u5b9e\u7528\u3002 \u503c\u5f97\u4e00\u63d0\u7684\u662f\uff0c\u8fd9\u95e8\u8bfe\u7684\u4f5c\u4e1a\u91cc\u4f60\u4f1a\u5b9e\u73b0\u4e00\u4e2a HashMap\uff08\u7c7b\u4f3c\u4e8e STL \u4e2d\u7684 unordered_map ), \u8fd9\u4e2a\u4f5c\u4e1a\u51e0\u4e4e\u628a\u6574\u4e2a\u8bfe\u7a0b\u4e32\u8054\u4e86\u8d77\u6765\uff0c\u975e\u5e38\u8003\u9a8c\u4ee3\u7801\u80fd\u529b\u3002\u7279\u522b\u662f iterator \u7684\u5b9e\u73b0\uff0c\u505a\u5b8c\u8fd9\u4e2a\u4f5c\u4e1a\u6211\u5f00\u59cb\u7406\u89e3\u4e3a\u4ec0\u4e48 Linus \u5bf9 C/C++ \u55e4\u4e4b\u4ee5\u9f3b\u4e86\uff0c\u56e0\u4e3a\u771f\u7684\u5f88\u96be\u5199\u5bf9\u3002 \u603b\u7684\u6765\u8bb2\u8fd9\u95e8\u8bfe\u5e76\u4e0d\u96be\uff0c\u4f46\u662f\u4fe1\u606f\u91cf\u5f88\u5927\uff0c\u9700\u8981\u4f60\u5728\u4e4b\u540e\u7684\u5f00\u53d1\u5b9e\u8df5\u4e2d\u53cd\u590d\u5de9\u56fa\u3002Stanford \u4e4b\u6240\u4ee5\u5355\u5f00\u4e00\u95e8 C++ \u7684\u7f16\u7a0b\u8bfe\uff0c\u662f\u56e0\u4e3a\u5b83\u540e\u7eed\u7684\u5f88\u591a CS \u8bfe\u7a0b Project \u90fd\u662f\u57fa\u4e8e C++\u7684\u3002\u4f8b\u5982 CS144 \u8ba1\u7b97\u673a\u7f51\u7edc\u548c CS143 \u7f16\u8bd1\u5668\u3002\u8fd9\u4e24\u95e8\u8bfe\u5728\u672c\u4e66\u4e2d\u5747\u6709\u6536\u5f55\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs106l/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/channel/UCSqr6y-eaQT_qZJVUm_4QxQ/playlists \u8bfe\u7a0b\u6559\u6750\uff1a http://web.stanford.edu/class/cs106l/full_course_reader.pdf Assignment1\u4e0b\u8f7d\u7f51\u5740\uff1a https://github.com/snme/cs106L-assignment1 Assignment2\u4e0b\u8f7d\u7f51\u5740\uff1a https://github.com/snme/cs106L-assignment2 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u5177\u4f53\u5185\u5bb9\u89c1\u8bfe\u7a0b\u7f51\u7ad9\uff0c\u6211\u505a\u7684\u65f6\u5019\u4e00\u5171\u662f\u4e24\u4e2a\uff1a \u5b9e\u73b0\u4e00\u4e2a WikiRacer \u7684\u5c0f\u6e38\u620f \u5b9e\u73b0\u4e00\u4e2a\u7c7b\u4f3c STL \u5e93\u7684 HashMap \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS106L - GitHub \u4e2d\u3002","title":"Stanford CS106L: Standard C++ Programming"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#cs106l-standard-c-programming","text":"","title":"CS106L: Standard C++ Programming"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6700\u597d\u638c\u63e1\u81f3\u5c11\u4e00\u95e8\u7f16\u7a0b\u8bed\u8a00 \u7f16\u7a0b\u8bed\u8a00\uff1aC++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a20 \u5c0f\u65f6 \u6211\u4ece\u5927\u4e00\u5f00\u59cb\u4e00\u76f4\u90fd\u662f\u5199\u7684 C++ \u4ee3\u7801\uff0c\u76f4\u5230\u5b66\u5b8c\u8fd9\u95e8\u8bfe\u6211\u624d\u610f\u8bc6\u5230\uff0c\u6211\u5199\u7684 C++ \u4ee3\u7801\u5927\u6982\u53ea\u662f C \u8bed\u8a00 + cin / cout \u800c\u5df2\u3002 \u8fd9\u95e8\u8bfe\u4f1a\u6df1\u5165\u5230\u5f88\u591a\u6807\u51c6 C++ \u7684\u7279\u6027\u548c\u8bed\u6cd5\uff0c\u8ba9\u4f60\u7f16\u5199\u51fa\u9ad8\u8d28\u91cf\u7684 C++ \u4ee3\u7801\u3002\u4f8b\u5982 auto binding, uniform initialization, lambda function, move semantics\uff0cRAII \u7b49\u6280\u5de7\u90fd\u5728\u6211\u6b64\u540e\u7684\u4ee3\u7801\u751f\u6daf\u4e2d\u88ab\u53cd\u590d\u7528\u5230\uff0c\u975e\u5e38\u5b9e\u7528\u3002 \u503c\u5f97\u4e00\u63d0\u7684\u662f\uff0c\u8fd9\u95e8\u8bfe\u7684\u4f5c\u4e1a\u91cc\u4f60\u4f1a\u5b9e\u73b0\u4e00\u4e2a HashMap\uff08\u7c7b\u4f3c\u4e8e STL \u4e2d\u7684 unordered_map ), \u8fd9\u4e2a\u4f5c\u4e1a\u51e0\u4e4e\u628a\u6574\u4e2a\u8bfe\u7a0b\u4e32\u8054\u4e86\u8d77\u6765\uff0c\u975e\u5e38\u8003\u9a8c\u4ee3\u7801\u80fd\u529b\u3002\u7279\u522b\u662f iterator \u7684\u5b9e\u73b0\uff0c\u505a\u5b8c\u8fd9\u4e2a\u4f5c\u4e1a\u6211\u5f00\u59cb\u7406\u89e3\u4e3a\u4ec0\u4e48 Linus \u5bf9 C/C++ \u55e4\u4e4b\u4ee5\u9f3b\u4e86\uff0c\u56e0\u4e3a\u771f\u7684\u5f88\u96be\u5199\u5bf9\u3002 \u603b\u7684\u6765\u8bb2\u8fd9\u95e8\u8bfe\u5e76\u4e0d\u96be\uff0c\u4f46\u662f\u4fe1\u606f\u91cf\u5f88\u5927\uff0c\u9700\u8981\u4f60\u5728\u4e4b\u540e\u7684\u5f00\u53d1\u5b9e\u8df5\u4e2d\u53cd\u590d\u5de9\u56fa\u3002Stanford \u4e4b\u6240\u4ee5\u5355\u5f00\u4e00\u95e8 C++ \u7684\u7f16\u7a0b\u8bfe\uff0c\u662f\u56e0\u4e3a\u5b83\u540e\u7eed\u7684\u5f88\u591a CS \u8bfe\u7a0b Project \u90fd\u662f\u57fa\u4e8e C++\u7684\u3002\u4f8b\u5982 CS144 \u8ba1\u7b97\u673a\u7f51\u7edc\u548c CS143 \u7f16\u8bd1\u5668\u3002\u8fd9\u4e24\u95e8\u8bfe\u5728\u672c\u4e66\u4e2d\u5747\u6709\u6536\u5f55\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs106l/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/channel/UCSqr6y-eaQT_qZJVUm_4QxQ/playlists \u8bfe\u7a0b\u6559\u6750\uff1a http://web.stanford.edu/class/cs106l/full_course_reader.pdf Assignment1\u4e0b\u8f7d\u7f51\u5740\uff1a https://github.com/snme/cs106L-assignment1 Assignment2\u4e0b\u8f7d\u7f51\u5740\uff1a https://github.com/snme/cs106L-assignment2 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u5177\u4f53\u5185\u5bb9\u89c1\u8bfe\u7a0b\u7f51\u7ad9\uff0c\u6211\u505a\u7684\u65f6\u5019\u4e00\u5171\u662f\u4e24\u4e2a\uff1a \u5b9e\u73b0\u4e00\u4e2a WikiRacer \u7684\u5c0f\u6e38\u620f \u5b9e\u73b0\u4e00\u4e2a\u7c7b\u4f3c STL \u5e93\u7684 HashMap","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS106L - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS110L/","text":"CS110L: Safety in Systems Programming \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6700\u597d\u6709\u4e00\u5b9a\u7684\u7f16\u7a0b\u80cc\u666f\u5e76\u5bf9\u8ba1\u7b97\u673a\u7cfb\u7edf\u6709\u521d\u6b65\u7684\u8ba4\u8bc6\u3002 \u7f16\u7a0b\u8bed\u8a00\uff1aRust \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a30 \u5c0f\u65f6 \u5728\u8fd9\u95e8\u8bfe\u4e2d\u4f60\u5c06\u4f1a\u5b66\u4e60 Rust \u8fd9\u95e8\u795e\u5947\u7684\u8bed\u8a00\u3002 \u5982\u679c\u4f60\u5b66\u8fc7 C \u5e76\u63a5\u89e6\u8fc7\u4e00\u4e9b\u7cfb\u7edf\u7f16\u7a0b\u7684\u8bdd\uff0c\u5e94\u8be5\u5bf9 C \u7684\u5185\u5b58\u6cc4\u6f0f\u4ee5\u53ca\u6307\u9488\u7684\u5371\u9669\u6709\u6240\u8033\u95fb\uff0c\u4f46 C \u7684\u5e95\u5c42\u7279\u6027\u4ee5\u53ca\u9ad8\u6548\u4ecd\u7136\u8ba9\u5b83\u5728\u7cfb\u7edf\u7ea7\u7f16\u7a0b\u4e2d\u65e0\u6cd5\u88ab\u4f8b\u5982 Java \u7b49\u81ea\u5e26\u5783\u573e\u6536\u96c6\u673a\u5236\u7684\u9ad8\u7ea7\u8bed\u8a00\u6240\u66ff\u4ee3\u3002\u800c Rust \u7684\u76ee\u6807\u5219\u662f\u5e0c\u671b\u5728 C \u7684\u9ad8\u6548\u57fa\u7840\u4e0a\uff0c\u5f25\u8865\u5176\u5b89\u5168\u4e0d\u8db3\u7684\u7f3a\u70b9\u3002\u56e0\u6b64 Rust \u5728\u8bbe\u8ba1\u4e4b\u521d\uff0c\u5c31\u6709\u5e26\u6709\u5f88\u591a\u7cfb\u7edf\u7f16\u7a0b\u7684\u89c2\u70b9\u3002\u5b66\u4e60 Rust\uff0c\u4e5f\u80fd\u8ba9\u4f60\u4e4b\u540e\u80fd\u7528 C \u8bed\u8a00\u7f16\u5199\u51fa\u66f4\u5b89\u5168\u66f4\u4f18\u96c5\u7684\u7cfb\u7edf\u7ea7\u4ee3\u7801\uff08\u4f8b\u5982\u64cd\u4f5c\u7cfb\u7edf\u7b49\uff09\u3002 \u8fd9\u95e8\u8bfe\u7684\u540e\u534a\u90e8\u5206\u5173\u6ce8\u5728\u5e76\u53d1\uff08concurrency\uff09\u8fd9\u4e00\u4e3b\u9898\u4e0a\uff0c\u4f60\u5c06\u4f1a\u7cfb\u7edf\u5730\u638c\u63e1\u591a\u8fdb\u7a0b\u3001\u591a\u7ebf\u7a0b\u3001\u57fa\u4e8e\u4e8b\u4ef6\u9a71\u52a8\u7684\u5e76\u53d1\u7b49\u82e5\u5e72\u5e76\u53d1\u6280\u672f\uff0c\u5e76\u5728\u7b2c\u4e8c\u4e2a Project \u4e2d\u6bd4\u8f83\u5b83\u4eec\u5404\u81ea\u7684\u4f18\u52a3\u3002Rust \u4e2d \u201cfutures\u201d \u7684\u6982\u5ff5\u975e\u5e38\u6709\u8da3\u548c\u4f18\u96c5\uff0c\u8fd9\u4e9b\u57fa\u7840\u77e5\u8bc6\u5bf9\u4f60\u540e\u7eed\u5bf9\u8ba1\u7b97\u673a\u7cfb\u7edf\u76f8\u5173\u8bfe\u7a0b\u7684\u5b66\u4e60\u5f88\u6709\u5e2e\u52a9\u3002\u53e6\u5916\uff0c\u6e05\u534e\u5927\u5b66\u7684\u64cd\u7edf\u5b9e\u9a8c rCore \u5c31\u662f\u57fa\u4e8e Rust \u7f16\u5199\u7684\uff0c\u5177\u4f53\u53c2\u89c1 \u6587\u6863 \u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://reberhardt.com/cs110l/spring-2020/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://youtu.be/j7AQrtLevUE \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u5171 6 \u4e2a Lab \u548c 2 \u4e2a Project\uff0c\u4f5c\u4e1a\u6587\u6863\u548c\u4ee3\u7801\u6846\u67b6\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9\u3002\u5176\u4e2d\u4e24\u4e2a Project \u975e\u5e38\u6709\u8da3\uff0c\u5206\u522b\u662f\uff1a \u7528 Rust \u5b9e\u73b0\u4e00\u4e2a\u7c7b\u4f3c\u4e8e GDB \u7684 debugger \u7528 Rust 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\u8fd9\u95e8\u795e\u5947\u7684\u8bed\u8a00\u3002 \u5982\u679c\u4f60\u5b66\u8fc7 C \u5e76\u63a5\u89e6\u8fc7\u4e00\u4e9b\u7cfb\u7edf\u7f16\u7a0b\u7684\u8bdd\uff0c\u5e94\u8be5\u5bf9 C \u7684\u5185\u5b58\u6cc4\u6f0f\u4ee5\u53ca\u6307\u9488\u7684\u5371\u9669\u6709\u6240\u8033\u95fb\uff0c\u4f46 C \u7684\u5e95\u5c42\u7279\u6027\u4ee5\u53ca\u9ad8\u6548\u4ecd\u7136\u8ba9\u5b83\u5728\u7cfb\u7edf\u7ea7\u7f16\u7a0b\u4e2d\u65e0\u6cd5\u88ab\u4f8b\u5982 Java \u7b49\u81ea\u5e26\u5783\u573e\u6536\u96c6\u673a\u5236\u7684\u9ad8\u7ea7\u8bed\u8a00\u6240\u66ff\u4ee3\u3002\u800c Rust \u7684\u76ee\u6807\u5219\u662f\u5e0c\u671b\u5728 C \u7684\u9ad8\u6548\u57fa\u7840\u4e0a\uff0c\u5f25\u8865\u5176\u5b89\u5168\u4e0d\u8db3\u7684\u7f3a\u70b9\u3002\u56e0\u6b64 Rust \u5728\u8bbe\u8ba1\u4e4b\u521d\uff0c\u5c31\u6709\u5e26\u6709\u5f88\u591a\u7cfb\u7edf\u7f16\u7a0b\u7684\u89c2\u70b9\u3002\u5b66\u4e60 Rust\uff0c\u4e5f\u80fd\u8ba9\u4f60\u4e4b\u540e\u80fd\u7528 C 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\u7f16\u7a0b\u8bed\u8a00\uff1aC++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a60 \u5c0f\u65f6 \u5b98\u65b9\u4ecb\u7ecd: \u672c\u8bfe\u7a0b\u5c06\u5168\u9762\u5730\u4ecb\u7ecd\u73b0\u4ee3\u5b9e\u65f6\u6e32\u67d3\u4e2d\u7684\u5173\u952e\u95ee\u9898\u4e0e\u89e3\u51b3\u65b9\u6cd5\u3002\u7531\u4e8e\u5b9e\u65f6\u6e32\u67d3 (>30 FPS) \u5bf9\u901f\u5ea6\u8981\u6c42\u6781\u9ad8\uff0c\u56e0\u6b64\u672c\u8bfe\u7a0b\u7684\u5173\u6ce8\u70b9\u5c06\u662f\u5728\u82db\u523b\u7684\u65f6\u95f4\u9650\u5236\u4e0b\uff0c\u4eba\u4eec\u5982\u4f55\u6253\u7834\u901f\u5ea6\u4e0e\u8d28\u91cf\u4e4b\u95f4\u7684\u6743\u8861\uff0c\u540c\u65f6\u4fdd\u8bc1\u5b9e\u65f6\u7684\u9ad8\u901f\u5ea6\u4e0e\u7167\u7247\u7ea7\u7684\u771f\u5b9e\u611f\u3002 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Approach"},{"location":"%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/topdown_ustc/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1a\u4e2d\u56fd\u79d1\u5b66\u6280\u672f\u5927\u5b66 \u6388\u8bfe\u6559\u5e08\uff1a\u90d1\u70c7\u3001\u6768\u575a \u5148\u4fee\u8981\u6c42\uff1a\u64cd\u4f5c\u7cfb\u7edf\uff08\u975e\u5fc5\u9700\uff09 \u7f16\u7a0b\u8bed\u8a00\uff1a\u65e0 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a40 \u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u5e94\u8be5\u662f\u4e2d\u6587\u4e92\u8054\u7f51\u4e0a\u6bd4\u8f83\u706b\u7684\u8ba1\u7b97\u673a\u7f51\u7edc\u8bfe\u4e86\uff0c\u6559\u6750\u91c7\u7528\u795e\u4e66\u8ba1\u7b97\u673a\u7f51\u7edc\uff08\u81ea\u9876\u5411\u4e0b\u65b9\u6cd5\uff09\uff0c\u6388\u8bfe\u98ce\u683c\u66f4\u504f\u5411\u5b9e\u9645\u800c\u975e\u7eaf\u7406\u8bba\uff08 \u5f3a\u70c8\u5efa\u8bae \u5148\u9605\u8bfb\u6559\u6750\u9884\u4e60\u518d\u770b\u8bfe\uff0c\u5426\u5219\u4e0a\u8bfe\u65f6 \u53ef\u80fd 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\u7b2c7\u7248\uff09\uff0c\u673a\u68b0\u5de5\u4e1a\u51fa\u7248\u793e\uff0c2016","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/6031/","text":"MIT 6.031: Software Construction \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u638c\u63e1\u81f3\u5c11\u4e00\u95e8\u7f16\u7a0b\u8bed\u8a00 \u7f16\u7a0b\u8bed\u8a00\uff1aJava \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u7684\u76ee\u6807\u5c31\u662f\u8ba9\u5b66\u751f\u5b66\u4f1a\u5982\u4f55\u5199\u51fa\u9ad8\u8d28\u91cf\u7684\u4ee3\u7801\uff0c\u6240\u8c13\u9ad8\u8d28\u91cf\uff0c\u5219\u662f\u6ee1\u8db3\u4e0b\u9762\u4e09\u4e2a\u76ee\u6807\uff08\u8bfe\u7a0b\u8bbe\u8ba1\u8005\u539f\u8bdd\u590d\u5236\uff0c\u4ee5\u9632\u81ea\u5df1\u7ffb\u8bd1\u66f2\u89e3\u672c\u610f\uff09\uff1a Safe from bugs. Correctness (correct behavior right now) and defensiveness (correct behavior in the future) are required in any software we build. Easy to understand. The code has to communicate to future programmers who need to understand it and make changes in it (fixing bugs or adding new features). That future programmer might be you, months or years from now. You\u2019ll be surprised how much you forget if you don\u2019t write it down, and how much it helps your own future self to have a good design. Ready for change. Software always changes. Some designs make it easy to make changes; others require throwing away and rewriting a lot of code. \u4e3a\u6b64\uff0c\u8fd9\u95e8\u8bfe\u7684\u8bbe\u8ba1\u8005\u4eec\u7cbe\u5fc3\u7f16\u5199\u4e86\u4e00\u672c\u4e66\u6765\u9610\u91ca\u8bf8\u591a\u8f6f\u4ef6\u6784\u5efa\u7684\u6838\u5fc3\u539f\u5219\u4e0e\u524d\u4eba\u603b\u7ed3\u4e0b\u6765\u7684\u5b9d\u8d35\u7ecf\u9a8c\uff0c\u5185\u5bb9\u7ec6\u8282\u5230\u5982\u4f55\u7f16\u5199\u6ce8\u91ca\u548c\u51fd\u6570 Specification\uff0c\u5982\u4f55\u8bbe\u8ba1\u62bd\u8c61\u6570\u636e\u7ed3\u6784\u4ee5\u53ca\u8bf8\u591a\u5e76\u884c\u7f16\u7a0b\u7684\u5185\u5bb9\uff0c\u5e76\u4e14\u4f1a\u8ba9\u4f60\u5728\u7cbe\u5fc3\u8bbe\u8ba1\u7684 Java \u7f16\u7a0b\u9879\u76ee\u91cc\u4f53\u9a8c\u548c\u7ec3\u4e60\u8fd9\u4e9b\u7f16\u7a0b\u6a21\u5f0f\u3002 2016\u5e74\u6625\u5b63\u5b66\u671f\u8fd9\u95e8\u8bfe\u5f00\u6e90\u4e86\u5176\u6240\u6709\u7f16\u7a0b\u4f5c\u4e1a\u7684\u4ee3\u7801\u6846\u67b6\uff0c\u800c\u6700\u65b0\u7684\u8bfe\u7a0b\u6559\u6750\u53ef\u4ee5\u5728\u5176\u6700\u65b0\u7684\u6559\u5b66\u7f51\u7ad9\u4e0a\u627e\u5230\uff0c\u5177\u4f53\u94fe\u63a5\u53c2\u89c1\u4e0b\u65b9\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a 2021spring , 2016spring \u8bfe\u7a0b\u89c6\u9891\uff1a\u65e0 \u8bfe\u7a0b\u6559\u6750\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9\u7684\u8bfe\u7a0b notes \u8bfe\u7a0b\u4f5c\u4e1a\uff1a4 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a + 1 \u4e2a Project \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/MIT6.031-software-construction - GitHub \u4e2d\u3002 @pengzhangzhi \u5b8c\u6210\u4e86\u8fd9\u95e8\u8bfe\u7684\u4f5c\u4e1a\u5e76\u8bb0\u5f55\u4e86\u7b14\u8bb0, \u4ee3\u7801\u5f00\u6e90\u5728 pengzhangzhi/self-taught-CS/Software Construction - Github \u3002","title":"MIT 6.031: Software Construction"},{"location":"%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/6031/#mit-6031-software-construction","text":"","title":"MIT 6.031: Software Construction"},{"location":"%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/6031/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u638c\u63e1\u81f3\u5c11\u4e00\u95e8\u7f16\u7a0b\u8bed\u8a00 \u7f16\u7a0b\u8bed\u8a00\uff1aJava \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u7684\u76ee\u6807\u5c31\u662f\u8ba9\u5b66\u751f\u5b66\u4f1a\u5982\u4f55\u5199\u51fa\u9ad8\u8d28\u91cf\u7684\u4ee3\u7801\uff0c\u6240\u8c13\u9ad8\u8d28\u91cf\uff0c\u5219\u662f\u6ee1\u8db3\u4e0b\u9762\u4e09\u4e2a\u76ee\u6807\uff08\u8bfe\u7a0b\u8bbe\u8ba1\u8005\u539f\u8bdd\u590d\u5236\uff0c\u4ee5\u9632\u81ea\u5df1\u7ffb\u8bd1\u66f2\u89e3\u672c\u610f\uff09\uff1a Safe from bugs. Correctness (correct behavior right now) and defensiveness (correct behavior in the future) are required in any software we build. Easy to understand. The code has to communicate to future programmers who need to understand it and make changes in it (fixing bugs or adding new features). That future programmer might be you, months or years from now. You\u2019ll be surprised how much you forget if you don\u2019t write it down, and how much it helps your own future self to have a good design. Ready for change. Software always changes. Some designs make it easy to make changes; others require throwing away and rewriting a lot of code. \u4e3a\u6b64\uff0c\u8fd9\u95e8\u8bfe\u7684\u8bbe\u8ba1\u8005\u4eec\u7cbe\u5fc3\u7f16\u5199\u4e86\u4e00\u672c\u4e66\u6765\u9610\u91ca\u8bf8\u591a\u8f6f\u4ef6\u6784\u5efa\u7684\u6838\u5fc3\u539f\u5219\u4e0e\u524d\u4eba\u603b\u7ed3\u4e0b\u6765\u7684\u5b9d\u8d35\u7ecf\u9a8c\uff0c\u5185\u5bb9\u7ec6\u8282\u5230\u5982\u4f55\u7f16\u5199\u6ce8\u91ca\u548c\u51fd\u6570 Specification\uff0c\u5982\u4f55\u8bbe\u8ba1\u62bd\u8c61\u6570\u636e\u7ed3\u6784\u4ee5\u53ca\u8bf8\u591a\u5e76\u884c\u7f16\u7a0b\u7684\u5185\u5bb9\uff0c\u5e76\u4e14\u4f1a\u8ba9\u4f60\u5728\u7cbe\u5fc3\u8bbe\u8ba1\u7684 Java \u7f16\u7a0b\u9879\u76ee\u91cc\u4f53\u9a8c\u548c\u7ec3\u4e60\u8fd9\u4e9b\u7f16\u7a0b\u6a21\u5f0f\u3002 2016\u5e74\u6625\u5b63\u5b66\u671f\u8fd9\u95e8\u8bfe\u5f00\u6e90\u4e86\u5176\u6240\u6709\u7f16\u7a0b\u4f5c\u4e1a\u7684\u4ee3\u7801\u6846\u67b6\uff0c\u800c\u6700\u65b0\u7684\u8bfe\u7a0b\u6559\u6750\u53ef\u4ee5\u5728\u5176\u6700\u65b0\u7684\u6559\u5b66\u7f51\u7ad9\u4e0a\u627e\u5230\uff0c\u5177\u4f53\u94fe\u63a5\u53c2\u89c1\u4e0b\u65b9\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/6031/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a 2021spring , 2016spring \u8bfe\u7a0b\u89c6\u9891\uff1a\u65e0 \u8bfe\u7a0b\u6559\u6750\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9\u7684\u8bfe\u7a0b notes \u8bfe\u7a0b\u4f5c\u4e1a\uff1a4 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a + 1 \u4e2a Project","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/6031/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/MIT6.031-software-construction - 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GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/","text":"Foreword The English version is still under development, please check this issue if you want to contribute. This is a self-learning guide to computer science, and a memento of my three years of self-learning at university. It is also a gift to the young students at Peking University. It would be a great encouragement and comfort to me if this book could be of even the slightest help to you in your college life. The book is currently organized to include the following sections (if you have other good suggestions, or would like to join the ranks of contributors, please feel free to email zhongyinmin@pku.edu.cn or ask questions in the issue). Productivity Toolkit: IDE, VPN, StackOverflow, Git, Github, Vim, Latex, GNU Make and so on. Environment configuration: PC/Server development environment setup, DevOps tutorials and so on. Book recommendations: Those who have read the CSAPP must have realized the importance of good books. I will list links to books and resources in different areas of Computer Science that I find rewarding to read. List of high quality CS courses : I will summarize all the high quality foreign CS courses I have taken into different categories and give relevant self-learning advice. Most of them will have a separate repository containing relevant resources as well as my homework/project implementations. The place where dreams start \u2014\u2014 CS61A In my freshman year, I was a novice who knew nothing about computers. I installed a giant IDE Visual Studio and fight with OJ every day. With my high school maths background, I did pretty well in maths courses, but I felt struggled to learn courses in my major. When it came to programming, all I could do was open up that clunky IDE, create a new project that I didn't know exactly what it was for, and then cin , cout , for loops, and then CE, RE, WA loops. I was in a state where I was desperately trying to learn well but I didn't know how to learn. I listened carefully in class but I couldn't solve the homework problems. I spent almost all my spare time doing the homework after class, but the results were disappointing. I still retain the source code of the project for Introduction to Computing course \u2014\u2014 a single 1200-line C++ file with no header files, no class abstraction, no unit tests, no makefile, no version control. The only good thing is that it can run, the disadvantage is the complement of \"can run\". For a while I wondered if I wasn't cut out for computer science, as all my childhood imaginings of geekiness had been completely ruined by my first semester's experience. It all turned around during the winter break of my freshman year, when I had a hankering to learn Python. I overheard someone recommend CS61A, a freshman introductory course at UC Berkeley on Python. I'll never forget that day, when I opened the CS61A course website. It was like Columbus discovering a new continent, and I opened the door to a new world. I finished the course in 3 weeks and for the first time I felt that CS could be so fulfilling and interesting, and I was shocked that there existed such a great course in the world. To avoid any suspicion of pandering to foreign courses, I will tell you about my experience of studying CS61A from the perspective of a pure student. Course website developed by course staffs : The course website integrates all the course resources into one, with a well organised course schedule, links to all slides, recorded videos and homework, detailed and clear syllabus, list of exams and solutions from previous years. Aesthetics aside, this website is so convenient for students. Textbook written by course instructor : The course instructor has adapted the classic MIT textbook Structure and Interpretation of Computer Programs (SICP) into Python (the original textbook was based on Scheme). This is a great way to ensure that the classroom content is consistent with the textbook, while adding more details. The entire book is open source and can be read directly online. Various, comprehensive and interesting homework : There are 14 labs to reinforce the knowledge gained in class, 10 homework assignments to practice, and 4 projects each with thousands of lines of code, all with well-organized skeleton code and babysitting instructions. Unlike the old-school OJ and Word document assignments, each lab/homework/project has a detailed handout document, fully automated grading scripts, and CS61A staffs have even developed an automated assignment submission and grading system . Of course, one might say \"How much can you learn from a project where most of code are written by your teaching assistants?\" . For someone who is new to CS and even stumbling over installing Python, this well-developed skeleton code allows students to focus on reinforcing the core knowledge they've learned in class, but also gives them a sense of achievement that they already can make a little game despite of learning Python only for a month. It also gives them the opportunity to read and learn from other people's high quality code so that they can reuse it later. I think in the freshman year, this kind of skeleton code is absolutely beneficial. The only bad thing perhaps is for the instructors and teaching assistants, as developing such assignments can conceivably require a considerable time commitment. Weekly discussion sessions : The teaching assistants will explain the difficult knowledge in class and add some supplementary materials which may not be covered in class. Also, there will be exercises from exams of previous years. All the exercises are written in LaTeX with solutions. In CS61A, You don't need any prerequesites about CS at all. You just need to pay attention, spend time and work hard. The feeling that you do not know what to do, that you are not getting anything in return for all the time you put in, is gone. It suited me so well that I fell in love with self-learning. Imagine that if someone could chew up the hard knowledge and present it to you in a vivid and straightforward way, with so many fancy and varied projects to reinforce your theoretical knowledge, you'd think they were really trying their best to make you fully grasp the course, and it was even an insult to the course builders not to learn it well. If you think I'm exaggerating, start with CS61A , because it's where my dreams began. Why write this book? In the 2020 Fall semester, I worked as a teaching assistant for the class Introduction to Computer Systems at Peking University. At that time, I had been studying totally on my own for over a year. I enjoyed this style of learning immensely. To share this joy, I have made a CS Self-learning Materials List for students in my seminar. It was purely on a whim at the time, as I wouldn't dare to encourage my students to skip classes and study on their own. But after another year of maintenance, the list has become quite comprehensive, covering most of the courses in Computer Science, Artificial Intelligence and Soft Engineering, and I have built separate repositories for each course, summarising the self-learning materials that I used. In my last college year, when I opened up my curriculum book, I realized that it was already a subset of my self-learning list. By then, it was only two and a half years after I had started my self-learning journey. Then, a bold idea came to my mind: perhaps I could create a self-learning book, write down the difficulty I encountered and the interest I found during these years of self-learning, hoping to make it easy for students who may also enjoy self-learning to start their wonderful self-learning journey. If you can build up the whole CS foundation in less than three years, have relatively solid mathematical skills and coding ability, experience dozens of projects with thousands of lines of code, master at least C/C++/Java/JS/Python/Go/Rust and other mainstream programming languages, have a good understanding of algorithms, circuits, architectures, networks, operating systems, compilers, artificial intelligence, machine learning, computer vision, natural language processing, reinforcement learning, cryptography, information theory, game theory, numerical analysis, statistics, distributed systems, parallel computing, database systems, computer graphics, web development, cloud computing, supercomputing etc. I think you will be confident enough to choose the area you are interested in, and you will be quite competitive in both industry and academia. I firmly believe that if you have read to this line, you do not lack the ability and committment to learn CS well, you just need a good teacher to teach you a good course. And I will try my best to pick such courses for you, based on my three years of experience. Pros For me, the biggest advantage of self-learning is that I can adjust the pace of learning entirely according to my own progress. For difficult parts, I can watch the videos over and over again, Google it online and ask questions on StackOverflow until I have it all figured out. For those that I mastered relatively quickly, I could skip them at twice or even three times the speed. Another great thing about self-learning is that you can learn from different perspectives. I have taken core courses such as architectures, networking, operating systems, and compilers from different universities. Different instructors may have different views on the same knowledge, which will broaden your horizon. A third advantage of self-learning is that you do not need to go to the class, listening to the boring lectures. Cons Of course, as a big fan of self-learning, I have to admit that it has its disadvantages. The first is the difficulty of communication. I'm actually a very keen questioner, and I like to follow up all the points I don't understand. But when you're facing a screen and you hear a teacher talking about something you don't understand, you can't go to the other end of the network and ask him or her for clarification. I try to mitigate this by thinking independently and making good use of Google, but it would be great to have a few friends to study together. You can refer to README for more information on participating a community group. The second thing is that these courses are basically in English. From the videos to the slides to the assignments, all in English. You may struggle at first, but I think it's a challenge that if you overcome, it will be extremely rewarding. Because at the moment, as reluctant as I am, I have to admit that in computer science, a lot of high quality documentation, forums and websites are all in English. The third, and I think the most difficult one, is self-discipline. Because have no DDL can sometimes be a really scary thing, especially when you get deeper, many foreign courses are quite difficult. You have to be self-driven enough to force yourself to settle down, read dozens of pages of Project Handout, understand thousands of lines of skeleton code and endure hours of debugging time. With no credits, no grades, no teachers, no classmates, just one belief - that you are getting better. Who is this book for? As I said in the beginning, anyone who is interested in learning computer science on their own can refer to this book. If you already have some basic skills and are just interested in a particular area, you can selectively pick and choose what you are interested in to study. Of course, if you are a novice who knows nothing about computers like I did back then, and just begin your college journey, I hope this book will be your cheat sheet to get the knowledge and skills you need in the least amount of time. In a way, this book is more like a course search engine ordered according to my experience, helping you to learn high quality CS courses from the world's top universities without leaving home. Of course, as an undergraduate student who has not yet graduated, I feel that I am not in a position nor have the right to preach one way of learning. I just hope that this material will help those who are also self-motivated and persistent to gain a richer, more varied and satisfying college life. Special thanks I would like to express my sincere gratitude to all the professors who have made their courses public for free. These courses are the culmination of decades of their teaching careers, and they have chosen to selflessly make such a high quality CS education available to all. Without them, my university life would not have been as fulfilling and enjoyable. Many of the professors would even reply with hundreds of words in length after I had sent them a thank you email, which really touched me beyond words. They also inspired me all the time that if decide to do something, do it with all heart and soul. Want to join as a contributor? There is a limit to how much one person can do, and this book was written by me under a heavy research schedule, so there are inevitably imperfections. In addition, as I work in the area of systems, many of the courses focus on systems, and there is relatively little content related to advanced mathematics, computing theory, and advanced algorithms. If any of you would like to share your self-learning experience and resources in other areas, you can directly initiate a Pull Request in the project, or feel free to contact me by email ( zhongyinmin@pku.edu.cn ).","title":"Foreword"},{"location":"en/#foreword","text":"The English version is still under development, please check this issue if you want to contribute. This is a self-learning guide to computer science, and a memento of my three years of self-learning at university. It is also a gift to the young students at Peking University. It would be a great encouragement and comfort to me if this book could be of even the slightest help to you in your college life. The book is currently organized to include the following sections (if you have other good suggestions, or would like to join the ranks of contributors, please feel free to email zhongyinmin@pku.edu.cn or ask questions in the issue). Productivity Toolkit: IDE, VPN, StackOverflow, Git, Github, Vim, Latex, GNU Make and so on. Environment configuration: PC/Server development environment setup, DevOps tutorials and so on. Book recommendations: Those who have read the CSAPP must have realized the importance of good books. I will list links to books and resources in different areas of Computer Science that I find rewarding to read. List of high quality CS courses : I will summarize all the high quality foreign CS courses I have taken into different categories and give relevant self-learning advice. Most of them will have a separate repository containing relevant resources as well as my homework/project implementations.","title":"Foreword"},{"location":"en/#the-place-where-dreams-start-cs61a","text":"In my freshman year, I was a novice who knew nothing about computers. I installed a giant IDE Visual Studio and fight with OJ every day. With my high school maths background, I did pretty well in maths courses, but I felt struggled to learn courses in my major. When it came to programming, all I could do was open up that clunky IDE, create a new project that I didn't know exactly what it was for, and then cin , cout , for loops, and then CE, RE, WA loops. I was in a state where I was desperately trying to learn well but I didn't know how to learn. I listened carefully in class but I couldn't solve the homework problems. I spent almost all my spare time doing the homework after class, but the results were disappointing. I still retain the source code of the project for Introduction to Computing course \u2014\u2014 a single 1200-line C++ file with no header files, no class abstraction, no unit tests, no makefile, no version control. The only good thing is that it can run, the disadvantage is the complement of \"can run\". For a while I wondered if I wasn't cut out for computer science, as all my childhood imaginings of geekiness had been completely ruined by my first semester's experience. It all turned around during the winter break of my freshman year, when I had a hankering to learn Python. I overheard someone recommend CS61A, a freshman introductory course at UC Berkeley on Python. I'll never forget that day, when I opened the CS61A course website. It was like Columbus discovering a new continent, and I opened the door to a new world. I finished the course in 3 weeks and for the first time I felt that CS could be so fulfilling and interesting, and I was shocked that there existed such a great course in the world. To avoid any suspicion of pandering to foreign courses, I will tell you about my experience of studying CS61A from the perspective of a pure student. Course website developed by course staffs : The course website integrates all the course resources into one, with a well organised course schedule, links to all slides, recorded videos and homework, detailed and clear syllabus, list of exams and solutions from previous years. Aesthetics aside, this website is so convenient for students. Textbook written by course instructor : The course instructor has adapted the classic MIT textbook Structure and Interpretation of Computer Programs (SICP) into Python (the original textbook was based on Scheme). This is a great way to ensure that the classroom content is consistent with the textbook, while adding more details. The entire book is open source and can be read directly online. Various, comprehensive and interesting homework : There are 14 labs to reinforce the knowledge gained in class, 10 homework assignments to practice, and 4 projects each with thousands of lines of code, all with well-organized skeleton code and babysitting instructions. Unlike the old-school OJ and Word document assignments, each lab/homework/project has a detailed handout document, fully automated grading scripts, and CS61A staffs have even developed an automated assignment submission and grading system . Of course, one might say \"How much can you learn from a project where most of code are written by your teaching assistants?\" . For someone who is new to CS and even stumbling over installing Python, this well-developed skeleton code allows students to focus on reinforcing the core knowledge they've learned in class, but also gives them a sense of achievement that they already can make a little game despite of learning Python only for a month. It also gives them the opportunity to read and learn from other people's high quality code so that they can reuse it later. I think in the freshman year, this kind of skeleton code is absolutely beneficial. The only bad thing perhaps is for the instructors and teaching assistants, as developing such assignments can conceivably require a considerable time commitment. Weekly discussion sessions : The teaching assistants will explain the difficult knowledge in class and add some supplementary materials which may not be covered in class. Also, there will be exercises from exams of previous years. All the exercises are written in LaTeX with solutions. In CS61A, You don't need any prerequesites about CS at all. You just need to pay attention, spend time and work hard. The feeling that you do not know what to do, that you are not getting anything in return for all the time you put in, is gone. It suited me so well that I fell in love with self-learning. Imagine that if someone could chew up the hard knowledge and present it to you in a vivid and straightforward way, with so many fancy and varied projects to reinforce your theoretical knowledge, you'd think they were really trying their best to make you fully grasp the course, and it was even an insult to the course builders not to learn it well. If you think I'm exaggerating, start with CS61A , because it's where my dreams began.","title":"The place where dreams start \u2014\u2014 CS61A"},{"location":"en/#why-write-this-book","text":"In the 2020 Fall semester, I worked as a teaching assistant for the class Introduction to Computer Systems at Peking University. At that time, I had been studying totally on my own for over a year. I enjoyed this style of learning immensely. To share this joy, I have made a CS Self-learning Materials List for students in my seminar. It was purely on a whim at the time, as I wouldn't dare to encourage my students to skip classes and study on their own. But after another year of maintenance, the list has become quite comprehensive, covering most of the courses in Computer Science, Artificial Intelligence and Soft Engineering, and I have built separate repositories for each course, summarising the self-learning materials that I used. In my last college year, when I opened up my curriculum book, I realized that it was already a subset of my self-learning list. By then, it was only two and a half years after I had started my self-learning journey. Then, a bold idea came to my mind: perhaps I could create a self-learning book, write down the difficulty I encountered and the interest I found during these years of self-learning, hoping to make it easy for students who may also enjoy self-learning to start their wonderful self-learning journey. If you can build up the whole CS foundation in less than three years, have relatively solid mathematical skills and coding ability, experience dozens of projects with thousands of lines of code, master at least C/C++/Java/JS/Python/Go/Rust and other mainstream programming languages, have a good understanding of algorithms, circuits, architectures, networks, operating systems, compilers, artificial intelligence, machine learning, computer vision, natural language processing, reinforcement learning, cryptography, information theory, game theory, numerical analysis, statistics, distributed systems, parallel computing, database systems, computer graphics, web development, cloud computing, supercomputing etc. I think you will be confident enough to choose the area you are interested in, and you will be quite competitive in both industry and academia. I firmly believe that if you have read to this line, you do not lack the ability and committment to learn CS well, you just need a good teacher to teach you a good course. And I will try my best to pick such courses for you, based on my three years of experience.","title":"Why write this book?"},{"location":"en/#pros","text":"For me, the biggest advantage of self-learning is that I can adjust the pace of learning entirely according to my own progress. For difficult parts, I can watch the videos over and over again, Google it online and ask questions on StackOverflow until I have it all figured out. For those that I mastered relatively quickly, I could skip them at twice or even three times the speed. Another great thing about self-learning is that you can learn from different perspectives. I have taken core courses such as architectures, networking, operating systems, and compilers from different universities. Different instructors may have different views on the same knowledge, which will broaden your horizon. A third advantage of self-learning is that you do not need to go to the class, listening to the boring lectures.","title":"Pros"},{"location":"en/#cons","text":"Of course, as a big fan of self-learning, I have to admit that it has its disadvantages. The first is the difficulty of communication. I'm actually a very keen questioner, and I like to follow up all the points I don't understand. But when you're facing a screen and you hear a teacher talking about something you don't understand, you can't go to the other end of the network and ask him or her for clarification. I try to mitigate this by thinking independently and making good use of Google, but it would be great to have a few friends to study together. You can refer to README for more information on participating a community group. The second thing is that these courses are basically in English. From the videos to the slides to the assignments, all in English. You may struggle at first, but I think it's a challenge that if you overcome, it will be extremely rewarding. Because at the moment, as reluctant as I am, I have to admit that in computer science, a lot of high quality documentation, forums and websites are all in English. The third, and I think the most difficult one, is self-discipline. Because have no DDL can sometimes be a really scary thing, especially when you get deeper, many foreign courses are quite difficult. You have to be self-driven enough to force yourself to settle down, read dozens of pages of Project Handout, understand thousands of lines of skeleton code and endure hours of debugging time. With no credits, no grades, no teachers, no classmates, just one belief - that you are getting better.","title":"Cons"},{"location":"en/#who-is-this-book-for","text":"As I said in the beginning, anyone who is interested in learning computer science on their own can refer to this book. If you already have some basic skills and are just interested in a particular area, you can selectively pick and choose what you are interested in to study. Of course, if you are a novice who knows nothing about computers like I did back then, and just begin your college journey, I hope this book will be your cheat sheet to get the knowledge and skills you need in the least amount of time. In a way, this book is more like a course search engine ordered according to my experience, helping you to learn high quality CS courses from the world's top universities without leaving home. Of course, as an undergraduate student who has not yet graduated, I feel that I am not in a position nor have the right to preach one way of learning. I just hope that this material will help those who are also self-motivated and persistent to gain a richer, more varied and satisfying college life.","title":"Who is this book for?"},{"location":"en/#special-thanks","text":"I would like to express my sincere gratitude to all the professors who have made their courses public for free. These courses are the culmination of decades of their teaching careers, and they have chosen to selflessly make such a high quality CS education available to all. Without them, my university life would not have been as fulfilling and enjoyable. Many of the professors would even reply with hundreds of words in length after I had sent them a thank you email, which really touched me beyond words. They also inspired me all the time that if decide to do something, do it with all heart and soul.","title":"Special thanks"},{"location":"en/#want-to-join-as-a-contributor","text":"There is a limit to how much one person can do, and this book was written by me under a heavy research schedule, so there are inevitably imperfections. In addition, as I work in the area of systems, many of the courses focus on systems, and there is relatively little content related to advanced mathematics, computing theory, and advanced algorithms. If any of you would like to share your self-learning experience and resources in other areas, you can directly initiate a Pull Request in the project, or feel free to contact me by email ( zhongyinmin@pku.edu.cn ).","title":"Want to join as a contributor?"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/","text":"\u4e00\u4e2a\u4ec5\u4f9b\u53c2\u8003\u7684 CS \u5b66\u4e60\u89c4\u5212 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Calculus Course \u548c 18.06: Linear Algebra \u7684\u8bfe\u7a0b notes\uff0c\u81f3\u5c11\u4e8e\u6211\u800c\u8a00\uff0c\u5b83\u5e2e\u52a9\u6211\u6df1\u523b\u7406\u89e3\u4e86\u5fae\u79ef\u5206\u548c\u7ebf\u6027\u4ee3\u6570\u7684\u8bb8\u591a\u672c\u8d28\u3002\u987a\u9053\u518d\u5b89\u5229\u4e00\u4e2a\u6cb9\u7ba1\u6570\u5b66\u7f51\u7ea2 3Blue1Brown \uff0c\u4ed6\u7684\u9891\u9053\u6709\u5f88\u591a\u7528\u751f\u52a8\u5f62\u8c61\u7684\u52a8\u753b\u9610\u91ca\u6570\u5b66\u672c\u8d28\u5185\u6838\u7684\u89c6\u9891\uff0c\u517c\u5177\u6df1\u5ea6\u548c\u5e7f\u5ea6\uff0c\u8d28\u91cf\u975e\u5e38\u9ad8\u3002 \u4fe1\u606f\u8bba\u5165\u95e8 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u53ca\u65e9\u4e86\u89e3\u4e00\u4e9b\u4fe1\u606f\u8bba\u7684\u57fa\u7840\u77e5\u8bc6\uff0c\u6211\u89c9\u5f97\u662f\u5927\u6709\u88e8\u76ca\u7684\u3002\u4f46\u5927\u591a\u4fe1\u606f\u8bba\u8bfe\u7a0b\u90fd\u9762\u5411\u9ad8\u5e74\u7ea7\u672c\u79d1\u751f\u751a\u81f3\u7814\u7a76\u751f\uff0c\u5bf9\u65b0\u624b\u6781\u4e0d\u53cb\u597d\u3002\u800c MIT \u7684 6.050J: Information theory and Entropy \u8fd9\u95e8\u8bfe\u6b63\u662f\u4e3a\u5927\u4e00\u65b0\u751f\u91cf\u8eab\u5b9a\u5236\u7684\uff0c\u51e0\u4e4e\u6ca1\u6709\u5148\u4fee\u8981\u6c42\uff0c\u6db5\u76d6\u4e86\u7f16\u7801\u3001\u538b\u7f29\u3001\u901a\u4fe1\u3001\u4fe1\u606f\u71b5\u7b49\u7b49\u5185\u5bb9\uff0c\u975e\u5e38\u6709\u8da3\u3002 \u6570\u5b66\u8fdb\u9636 \u79bb\u6563\u6570\u5b66\u4e0e\u6982\u7387\u8bba \u96c6\u5408\u8bba\u3001\u56fe\u8bba\u3001\u6982\u7387\u8bba\u7b49\u7b49\u662f\u7b97\u6cd5\u63a8\u5bfc\u4e0e\u8bc1\u660e\u7684\u91cd\u8981\u5de5\u5177\uff0c\u4e5f\u662f\u540e\u7eed\u9ad8\u9636\u6570\u5b66\u8bfe\u7a0b\u7684\u57fa\u7840\u3002\u4f46\u6211\u89c9\u5f97\u8fd9\u7c7b\u8bfe\u7a0b\u7684\u8bb2\u6388\u5f88\u5bb9\u6613\u843d\u5165\u7406\u8bba\u5316\u4e0e\u5f62\u5f0f\u5316\u7684\u7aa0\u81fc\uff0c\u8ba9\u8bfe\u5802\u6210\u4e3a\u5b9a\u7406\u7ed3\u8bba\u7684\u5806\u780c\uff0c\u800c\u65e0\u6cd5\u4f7f\u5b66\u751f\u6df1\u523b\u628a\u63e1\u7406\u8bba\u7684\u672c\u8d28\uff0c\u8fdb\u800c\u9020\u6210\u5b66\u4e86\u5c31\u80cc\uff0c\u8003\u4e86\u5c31\u5fd8\u7684\u602a\u5708\u3002\u5982\u679c\u80fd\u5728\u7406\u8bba\u6559\u5b66\u4e2d\u7a7f\u63d2\u7b97\u6cd5\u8fd0\u7528\u5b9e\u4f8b\uff0c\u5b66\u751f\u5728\u62d3\u5c55\u7b97\u6cd5\u77e5\u8bc6\u7684\u540c\u65f6\u4e5f\u80fd\u7aa5\u89c1\u7406\u8bba\u7684\u529b\u91cf\u548c\u9b45\u529b\u3002 UCB CS70 : discrete Math and probability theory \u548c UCB CS126 : Probability theory \u662f UC Berkeley \u7684\u6982\u7387\u8bba\u8bfe\u7a0b\uff0c\u524d\u8005\u8986\u76d6\u4e86\u79bb\u6563\u6570\u5b66\u548c\u6982\u7387\u8bba\u57fa\u7840\uff0c\u540e\u8005\u5219\u6d89\u53ca\u968f\u673a\u8fc7\u7a0b\u4ee5\u53ca\u6df1\u5165\u7684\u7406\u8bba\u5185\u5bb9\u3002\u4e24\u8005\u90fd\u975e\u5e38\u6ce8\u91cd\u7406\u8bba\u548c\u5b9e\u8df5\u7684\u7ed3\u5408\uff0c\u6709\u4e30\u5bcc\u7684\u7b97\u6cd5\u5b9e\u9645\u8fd0\u7528\u5b9e\u4f8b\uff0c\u540e\u8005\u8fd8\u6709\u5927\u91cf\u7684 Python \u7f16\u7a0b\u4f5c\u4e1a\u6765\u8ba9\u5b66\u751f\u8fd0\u7528\u6982\u7387\u8bba\u7684\u77e5\u8bc6\u89e3\u51b3\u5b9e\u9645\u95ee\u9898\u3002 \u6570\u503c\u5206\u6790 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u57f9\u517b\u8ba1\u7b97\u601d\u7ef4\u662f\u5f88\u91cd\u8981\u7684\uff0c\u5b9e\u9645\u95ee\u9898\u7684\u5efa\u6a21\u3001\u79bb\u6563\u5316\uff0c\u8ba1\u7b97\u673a\u7684\u6a21\u62df\u3001\u5206\u6790\uff0c\u662f\u4e00\u9879\u5f88\u91cd\u8981\u7684\u80fd\u529b\u3002\u800c\u8fd9\u4e24\u5e74\u5f00\u59cb\u98ce\u9761\u7684\uff0c\u7531 MIT \u6253\u9020\u7684 Julia \u7f16\u7a0b\u8bed\u8a00\u4ee5\u5176 C \u4e00\u6837\u7684\u901f\u5ea6\u548c Python \u4e00\u6837\u53cb\u597d\u7684\u8bed\u6cd5\u5728\u6570\u503c\u8ba1\u7b97\u9886\u57df\u6709\u4e00\u7edf\u5929\u4e0b\u4e4b\u52bf\uff0cMIT \u7684\u8bb8\u591a\u6570\u5b66\u8bfe\u7a0b\u4e5f\u5f00\u59cb\u7528 Julia \u4f5c\u4e3a\u6559\u5b66\u5de5\u5177\uff0c\u628a\u8270\u6df1\u7684\u6570\u5b66\u7406\u8bba\u7528\u76f4\u89c2\u6e05\u6670\u7684\u4ee3\u7801\u5c55\u793a\u51fa\u6765\u3002 ComputationalThinking \u662f MIT \u5f00\u8bbe\u7684\u4e00\u95e8\u8ba1\u7b97\u601d\u7ef4\u5165\u95e8\u8bfe\uff0c\u6240\u6709\u8bfe\u7a0b\u5185\u5bb9\u5168\u90e8\u5f00\u6e90\uff0c\u53ef\u4ee5\u5728\u8bfe\u7a0b\u7f51\u7ad9\u76f4\u63a5\u8bbf\u95ee\u3002\u8fd9\u95e8\u8bfe\u5229\u7528 Julia \u7f16\u7a0b\u8bed\u8a00\uff0c\u5728\u56fe\u50cf\u5904\u7406\u3001\u793e\u4f1a\u79d1\u5b66\u4e0e\u6570\u636e\u79d1\u5b66\u3001\u6c14\u5019\u5b66\u5efa\u6a21\u4e09\u4e2a topic \u4e0b\u5e26\u9886\u5b66\u751f\u7406\u89e3\u7b97\u6cd5\u3001\u6570\u5b66\u5efa\u6a21\u3001\u6570\u636e\u5206\u6790\u3001\u4ea4\u4e92\u8bbe\u8ba1\u3001\u56fe\u4f8b\u5c55\u793a\uff0c\u8ba9\u5b66\u751f\u4f53\u9a8c\u8ba1\u7b97\u4e0e\u79d1\u5b66\u7684\u7f8e\u5999\u7ed3\u5408\u3002\u5185\u5bb9\u867d\u7136\u4e0d\u96be\uff0c\u4f46\u7ed9\u6211\u6700\u6df1\u523b\u7684\u611f\u53d7\u5c31\u662f\uff0c\u79d1\u5b66\u7684\u9b45\u529b\u5e76\u4e0d\u662f\u6545\u5f04\u7384\u865a\u7684\u8270\u6df1\u7406\u8bba\uff0c\u4e0d\u662f\u8bd8\u5c48\u8071\u7259\u7684\u672f\u8bed\u884c\u8bdd\uff0c\u800c\u662f\u7528\u76f4\u89c2\u751f\u52a8\u7684\u6848\u4f8b\uff0c\u7528\u7b80\u7ec3\u6df1\u523b\u7684\u8bed\u8a00\uff0c\u8ba9\u6bcf\u4e2a\u666e\u901a\u4eba\u90fd\u80fd\u7406\u89e3\u3002 \u4e0a\u5b8c\u4e0a\u9762\u7684\u4f53\u9a8c\u8bfe\u4e4b\u540e\uff0c\u5982\u679c\u610f\u72b9\u672a\u5c3d\u7684\u8bdd\uff0c\u4e0d\u59a8\u8bd5\u8bd5 MIT \u7684 18.330 : Introduction to numerical analysis \uff0c\u8fd9\u95e8\u8bfe\u7684\u7f16\u7a0b\u4f5c\u4e1a\u540c\u6837\u4f1a\u7528 Julia \u7f16\u7a0b\u8bed\u8a00\uff0c\u4e0d\u8fc7\u96be\u5ea6\u548c\u6df1\u5ea6\u4e0a\u90fd\u4e0a\u4e86\u4e00\u4e2a\u53f0\u9636\u3002\u5185\u5bb9\u6d89\u53ca\u4e86\u6d6e\u70b9\u7f16\u7801\u3001Root finding\u3001\u7ebf\u6027\u7cfb\u7edf\u3001\u5fae\u5206\u65b9\u7a0b\u7b49\u7b49\u65b9\u9762\uff0c\u6574\u95e8\u8bfe\u7684\u4e3b\u65e8\u5c31\u662f\u8ba9\u4f60\u5229\u7528\u79bb\u6563\u5316\u7684\u8ba1\u7b97\u673a\u8868\u793a\u53bb\u4f30\u8ba1\u548c\u903c\u8fd1\u4e00\u4e2a\u6570\u5b66\u4e0a\u8fde\u7eed\u7684\u6982\u5ff5\u3002\u8fd9\u95e8\u8bfe\u7684\u6559\u6388\u8fd8\u4e13\u95e8\u64b0\u5199\u4e86\u4e00\u672c\u914d\u5957\u7684\u5f00\u6e90\u6559\u6750 Fundamentals of Numerical Computation \uff0c\u91cc\u9762\u9644\u6709\u4e30\u5bcc\u7684 Julia \u4ee3\u7801\u5b9e\u4f8b\u548c\u4e25\u8c28\u7684\u516c\u5f0f\u63a8\u5bfc\u3002 \u5982\u679c\u4f60\u8fd8\u610f\u72b9\u672a\u5c3d\u7684\u8bdd\uff0c\u8fd8\u6709 MIT \u7684\u6570\u503c\u5206\u6790\u7814\u7a76\u751f\u8bfe\u7a0b 18.335: Introduction to numerical method \u4f9b\u4f60\u53c2\u8003\u3002 \u5fae\u5206\u65b9\u7a0b \u5982\u679c\u4e16\u95f4\u4e07\u7269\u7684\u8fd0\u52a8\u53d1\u5c55\u90fd\u80fd\u7528\u65b9\u7a0b\u6765\u523b\u753b\u548c\u63cf\u8ff0\uff0c\u8fd9\u662f\u4e00\u4ef6\u591a\u4e48\u9177\u7684\u4e8b\u60c5\u5440\uff01\u867d\u7136\u51e0\u4e4e\u4efb\u4f55\u4e00\u6240\u5b66\u6821\u7684 CS \u57f9\u517b\u65b9\u6848\u4e2d\u90fd\u6ca1\u6709\u5fae\u5206\u65b9\u7a0b\u76f8\u5173\u7684\u5fc5\u4fee\u8bfe\u7a0b\uff0c\u4f46\u6211\u8fd8\u662f\u89c9\u5f97\u638c\u63e1\u5b83\u4f1a\u8d4b\u4e88\u4f60\u4e00\u4e2a\u65b0\u7684\u89c6\u89d2\u6765\u5ba1\u89c6\u8fd9\u4e2a\u4e16\u754c\u3002 \u7531\u4e8e\u5fae\u5206\u65b9\u7a0b\u4e2d\u5f80\u5f80\u4f1a\u7528\u5230\u5f88\u591a\u590d\u53d8\u51fd\u6570\u7684\u77e5\u8bc6\uff0c\u6240\u4ee5\u5927\u5bb6\u53ef\u4ee5\u53c2\u8003 MIT18.04: Complex variables functions \u7684\u8bfe\u7a0b notes \u6765\u8865\u9f50\u5148\u4fee\u77e5\u8bc6\u3002 MIT18.03: differential equations ) \u4e3b\u8981\u8986\u76d6\u4e86\u5e38\u5fae\u5206\u65b9\u7a0b\u7684\u6c42\u89e3\uff0c\u5728\u6b64\u57fa\u7840\u4e4b\u4e0a MIT18.152: Partial differential equations ) \u5219\u4f1a\u6df1\u5165\u504f\u5fae\u5206\u65b9\u7a0b\u7684\u5efa\u6a21\u4e0e\u6c42\u89e3\u3002\u638c\u63e1\u4e86\u5fae\u5206\u65b9\u7a0b\u8fd9\u4e00\u6709\u5229\u5de5\u5177\uff0c\u76f8\u4fe1\u5bf9\u4e8e\u4f60\u7684\u5b9e\u9645\u95ee\u9898\u7684\u5efa\u6a21\u80fd\u529b\u4ee5\u53ca\u4ece\u4f17\u591a\u566a\u58f0\u53d8\u91cf\u4e2d\u628a\u63e1\u672c\u8d28\u7684\u76f4\u89c9\u90fd\u4f1a\u6709\u5f88\u5927\u5e2e\u52a9\u3002 \u6570\u5b66\u9ad8\u9636 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u6211\u7ecf\u5e38\u542c\u5230\u6570\u5b66\u65e0\u7528\u8bba\u7684\u8bba\u65ad\uff0c\u5bf9\u6b64\u6211\u4e0d\u6562\u82df\u540c\u4f46\u4e5f\u65e0\u6743\u53cd\u5bf9\uff0c\u4f46\u82e5\u51e1\u4e8b\u90fd\u786c\u8981\u4e89\u51fa\u4e2a\u6709\u7528\u548c\u65e0\u7528\u7684\u533a\u522b\u6765\uff0c\u5012\u4e5f\u7740\u5b9e\u65e0\u8da3\uff0c\u56e0\u6b64\u4e0b\u9762\u8fd9\u4e9b\u9762\u5411\u9ad8\u5e74\u7ea7\u751a\u81f3\u7814\u7a76\u751f\u7684\u6570\u5b66\u8bfe\u7a0b\uff0c\u5927\u5bb6\u6309\u5174\u8da3\u81ea\u53d6\u6240\u9700\u3002 \u51f8\u4f18\u5316 Standford EE364A: Convex Optimization \u4fe1\u606f\u8bba MIT6.441: Information Theory \u5e94\u7528\u7edf\u8ba1\u5b66 MIT18.650: Statistics for Applications \u521d\u7b49\u6570\u8bba MIT18.781: Theory of Numbers \u5bc6\u7801\u5b66 Standford CS255: Cryptography \u7f16\u7a0b\u5165\u95e8 Languages are tools, you choose the right tool to do the right thing. Since there's no universally perfect tool, there's no universally perfect language. Shell MIT-Missing-Semester Python Harvard CS50: This is CS50x UCB CS61A: Structure and Interpretation of Computer Programs C++ Stanford CS106B/X: Programming Abstractions Stanford CS106L: Standard C++ Programming Rust Stanford CS110L: Safety in Systems Programming OCaml Cornell CS3110 textbook: Functional Programming in OCaml \u7535\u5b50\u57fa\u7840 \u7535\u8def\u57fa\u7840 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u4e86\u89e3\u4e00\u4e9b\u57fa\u7840\u7684\u7535\u8def\u77e5\u8bc6\uff0c\u611f\u53d7\u4ece\u4f20\u611f\u5668\u6536\u96c6\u6570\u636e\u5230\u6570\u636e\u5206\u6790\u518d\u5230\u7b97\u6cd5\u9884\u6d4b\u6574\u6761\u6d41\u6c34\u7ebf\uff0c\u5bf9\u4e8e\u540e\u7eed\u77e5\u8bc6\u7684\u5b66\u4e60\u4ee5\u53ca\u8ba1\u7b97\u601d\u7ef4\u7684\u57f9\u517b\u8fd8\u662f\u5f88\u6709\u5e2e\u52a9\u7684\u3002 EE16A&B: Designing Information Devices and Systems I&II \u662f\u4f2f\u514b\u5229 EE \u5b66\u751f\u7684\u5927\u4e00\u5165\u95e8\u8bfe\uff0c\u5176\u4e2d EE16A \u6ce8\u91cd\u901a\u8fc7\u7535\u8def\u4ece\u5b9e\u9645\u73af\u5883\u4e2d\u6536\u96c6\u548c\u5206\u6790\u6570\u636e\uff0c\u800c EE16B \u5219\u4fa7\u91cd\u4ece\u8fd9\u4e9b\u6536\u96c6\u5230\u7684\u6570\u636e\u8fdb\u884c\u5206\u6790\u5e76\u505a\u51fa\u9884\u6d4b\u884c\u4e3a\u3002 \u4fe1\u53f7\u4e0e\u7cfb\u7edf \u4fe1\u53f7\u4e0e\u7cfb\u7edf\u662f\u4e00\u95e8\u6211\u89c9\u5f97\u975e\u5e38\u503c\u5f97\u4e00\u4e0a\u7684\u8bfe\uff0c\u6700\u521d\u5b66\u5b83\u53ea\u662f\u4e3a\u4e86\u6ee1\u8db3\u6211\u5bf9\u5085\u91cc\u53f6\u53d8\u6362\u7684\u597d\u5947\uff0c\u4f46\u5b66\u5b8c\u4e4b\u540e\u6211\u624d\u4e0d\u7981\u611f\u53f9\uff0c\u5085\u7acb\u53f6\u53d8\u6362\u7ed9\u6211\u63d0\u4f9b\u4e86\u4e00\u4e2a\u5168\u65b0\u7684\u89c6\u89d2\u53bb\u770b\u5f85\u8fd9\u4e2a\u4e16\u754c\uff0c\u5c31\u5982\u540c\u5fae\u5206\u65b9\u7a0b\u4e00\u6837\uff0c\u8ba9\u4f60\u6c89\u6d78\u5728\u7528\u6570\u5b66\u53bb\u7cbe\u786e\u63cf\u7ed8\u548c\u523b\u753b\u8fd9\u4e2a\u4e16\u754c\u7684\u4f18\u96c5\u4e0e\u795e\u5947\u4e4b\u4e2d\u3002 MIT 6.003: signal and systems \u63d0\u4f9b\u4e86\u5168\u90e8\u7684\u8bfe\u7a0b\u5f55\u5f71\u3001\u4e66\u9762\u4f5c\u4e1a\u4ee5\u53ca\u7b54\u6848\u3002\u4e5f\u53ef\u4ee5\u53bb\u770b\u8fd9\u95e8\u8bfe\u7684 \u8fdc\u53e4\u7248\u672c \u800c UCB EE120: Signal and Systems \u5173\u4e8e\u5085\u7acb\u53f6\u53d8\u6362\u7684 notes \u5199\u5f97\u975e\u5e38\u597d\uff0c\u5e76\u4e14\u63d0\u4f9b\u4e866 \u4e2a\u975e\u5e38\u6709\u8da3\u7684 Python \u7f16\u7a0b\u4f5c\u4e1a\uff0c\u8ba9\u4f60\u5b9e\u8df5\u4e2d\u8fd0\u7528\u4fe1\u53f7\u4e0e\u7cfb\u7edf\u7684\u7406\u8bba\u4e0e\u7b97\u6cd5\u3002 \u6570\u636e\u7ed3\u6784\u4e0e\u7b97\u6cd5 \u6570\u636e\u7ed3\u6784\u4e0e\u7b97\u6cd5 UCB CS61B: Data Structures and Algorithms Coursera: Algorithms I & II \u7b97\u6cd5\u8bbe\u8ba1\u4e0e\u5206\u6790 UCB CS170: Efficient Algorithms and Intractable Problems \u8f6f\u4ef6\u5de5\u7a0b \u5165\u95e8\u8bfe \u4e00\u4efd\u201c\u80fd\u8dd1\u201d\u7684\u4ee3\u7801\uff0c\u548c\u4e00\u4efd\u9ad8\u8d28\u91cf\u7684\u5de5\u4e1a\u7ea7\u4ee3\u7801\u662f\u6709\u672c\u8d28\u533a\u522b\u7684\u3002\u56e0\u6b64\u6211\u975e\u5e38\u63a8\u8350\u4f4e\u5e74\u7ea7\u7684\u540c\u5b66\u5b66\u4e60\u4e00\u4e0b MIT 6.031: Software Construction \u8fd9\u95e8\u8bfe\uff0c\u5b83\u4f1a\u4ee5 Java \u8bed\u8a00\u4e3a\u57fa\u7840\uff0c\u4ee5\u4e30\u5bcc\u7ec6\u81f4\u7684\u9605\u8bfb\u6750\u6599\u548c\u7cbe\u5fc3\u8bbe\u8ba1\u7684\u7f16\u7a0b\u7ec3\u4e60\u4f20\u6388\u5982\u4f55\u7f16\u5199 \u4e0d\u6613\u51fa bug\u3001\u7b80\u660e\u6613\u61c2\u3001\u6613\u4e8e\u7ef4\u62a4\u4fee\u6539 \u7684\u9ad8\u8d28\u91cf\u4ee3\u7801\u3002\u5927\u5230\u5b8f\u89c2\u6570\u636e\u7ed3\u6784\u8bbe\u8ba1\uff0c\u5c0f\u5230\u5982\u4f55\u5199\u6ce8\u91ca\uff0c\u9075\u5faa\u8fd9\u4e9b\u524d\u4eba\u603b\u7ed3\u7684\u7ec6\u8282\u548c\u7ecf\u9a8c\uff0c\u5bf9\u4e8e\u4f60\u6b64\u540e\u7684\u7f16\u7a0b\u751f\u6daf\u5927\u6709\u88e8\u76ca\u3002 \u4e13\u4e1a\u8bfe \u5f53\u7136\uff0c\u5982\u679c\u4f60\u60f3\u7cfb\u7edf\u6027\u5730\u4e0a\u4e00\u95e8\u8f6f\u4ef6\u5de5\u7a0b\u7684\u8bfe\u7a0b\uff0c\u90a3\u6211\u63a8\u8350\u7684\u662f\u4f2f\u514b\u5229\u7684 UCB CS169: software engineering 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\u4e13\u4e1a\u8bfe \u5f53\u7136\uff0c\u5982\u679c\u60f3\u6df1\u5165\u73b0\u4ee3\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\u7684\u590d\u6742\u7ec6\u8282\uff0c\u8fd8\u5f97\u4e0a\u4e00\u95e8\u5927\u5b66\u672c\u79d1\u96be\u5ea6\u7684\u8bfe\u7a0b UCB CS61C: Great Ideas in Computer Architecture \u3002UC Berkeley \u4f5c\u4e3a RISC-V \u67b6\u6784\u7684\u53d1\u6e90\u5730\uff0c\u5728\u4f53\u7cfb\u7ed3\u6784\u9886\u57df\u7b97\u5f97\u4e0a\u9996\u5c48\u4e00\u6307\u3002\u5176\u8bfe\u7a0b\u975e\u5e38\u6ce8\u91cd\u5b9e\u8df5\uff0c\u4f60\u4f1a\u5728 Project \u4e2d\u624b\u5199\u6c47\u7f16\u6784\u9020\u795e\u7ecf\u7f51\u7edc\uff0c\u4ece\u96f6\u5f00\u59cb\u642d\u5efa\u4e00\u4e2a CPU\uff0c\u8fd9\u4e9b\u5b9e\u8df5\u90fd\u4f1a\u8ba9\u4f60\u5bf9\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\u6709\u66f4\u4e3a\u6df1\u5165\u7684\u7406\u89e3\uff0c\u800c\u4e0d\u662f\u4ec5\u505c\u7559\u4e8e\u201c\u53d6\u6307\u8bd1\u7801\u6267\u884c\u8bbf\u5b58\u5199\u56de\u201d\u7684\u5355\u8c03\u80cc\u8bf5\u91cc\u3002 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MIT6.033: System Engineering \u662f MIT \u7684\u7cfb\u7edf\u5165\u95e8\u8bfe\uff0c\u4e3b\u9898\u6d89\u53ca\u4e86\u64cd\u4f5c\u7cfb\u7edf\u3001\u7f51\u7edc\u3001\u5206\u5e03\u5f0f\u548c\u7cfb\u7edf\u5b89\u5168\uff0c\u9664\u4e86\u77e5\u8bc6\u70b9\u7684\u4f20\u6388\u5916\uff0c\u8fd9\u95e8\u8bfe\u8fd8\u4f1a\u8bb2\u6388\u4e00\u4e9b\u5199\u4f5c\u548c\u8868\u8fbe\u4e0a\u7684\u6280\u5de7\uff0c\u8ba9\u4f60\u5b66\u4f1a\u5982\u4f55\u8bbe\u8ba1\u5e76\u5411\u522b\u4eba\u4ecb\u7ecd\u548c\u5206\u6790\u81ea\u5df1\u7684\u7cfb\u7edf\u3002\u8fd9\u672c\u4e66\u914d\u5957\u7684\u6559\u6750 Principles of Computer System Design: An Introduction \u4e5f\u5199\u5f97\u975e\u5e38\u597d\uff0c\u63a8\u8350\u5927\u5bb6\u9605\u8bfb\u3002 CMU 15-213: Introduction to Computer System \u662f CMU 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\uff0c\u8fd9\u95e8\u8bfe\u7684\u7f16\u7a0b\u4f5c\u4e1a\u540c\u6837\u4f1a\u7528 Julia \u7f16\u7a0b\u8bed\u8a00\uff0c\u4e0d\u8fc7\u96be\u5ea6\u548c\u6df1\u5ea6\u4e0a\u90fd\u4e0a\u4e86\u4e00\u4e2a\u53f0\u9636\u3002\u5185\u5bb9\u6d89\u53ca\u4e86\u6d6e\u70b9\u7f16\u7801\u3001Root finding\u3001\u7ebf\u6027\u7cfb\u7edf\u3001\u5fae\u5206\u65b9\u7a0b\u7b49\u7b49\u65b9\u9762\uff0c\u6574\u95e8\u8bfe\u7684\u4e3b\u65e8\u5c31\u662f\u8ba9\u4f60\u5229\u7528\u79bb\u6563\u5316\u7684\u8ba1\u7b97\u673a\u8868\u793a\u53bb\u4f30\u8ba1\u548c\u903c\u8fd1\u4e00\u4e2a\u6570\u5b66\u4e0a\u8fde\u7eed\u7684\u6982\u5ff5\u3002\u8fd9\u95e8\u8bfe\u7684\u6559\u6388\u8fd8\u4e13\u95e8\u64b0\u5199\u4e86\u4e00\u672c\u914d\u5957\u7684\u5f00\u6e90\u6559\u6750 Fundamentals of Numerical Computation \uff0c\u91cc\u9762\u9644\u6709\u4e30\u5bcc\u7684 Julia \u4ee3\u7801\u5b9e\u4f8b\u548c\u4e25\u8c28\u7684\u516c\u5f0f\u63a8\u5bfc\u3002 \u5982\u679c\u4f60\u8fd8\u610f\u72b9\u672a\u5c3d\u7684\u8bdd\uff0c\u8fd8\u6709 MIT \u7684\u6570\u503c\u5206\u6790\u7814\u7a76\u751f\u8bfe\u7a0b 18.335: Introduction to numerical method \u4f9b\u4f60\u53c2\u8003\u3002","title":"\u6570\u503c\u5206\u6790"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_12","text":"\u5982\u679c\u4e16\u95f4\u4e07\u7269\u7684\u8fd0\u52a8\u53d1\u5c55\u90fd\u80fd\u7528\u65b9\u7a0b\u6765\u523b\u753b\u548c\u63cf\u8ff0\uff0c\u8fd9\u662f\u4e00\u4ef6\u591a\u4e48\u9177\u7684\u4e8b\u60c5\u5440\uff01\u867d\u7136\u51e0\u4e4e\u4efb\u4f55\u4e00\u6240\u5b66\u6821\u7684 CS \u57f9\u517b\u65b9\u6848\u4e2d\u90fd\u6ca1\u6709\u5fae\u5206\u65b9\u7a0b\u76f8\u5173\u7684\u5fc5\u4fee\u8bfe\u7a0b\uff0c\u4f46\u6211\u8fd8\u662f\u89c9\u5f97\u638c\u63e1\u5b83\u4f1a\u8d4b\u4e88\u4f60\u4e00\u4e2a\u65b0\u7684\u89c6\u89d2\u6765\u5ba1\u89c6\u8fd9\u4e2a\u4e16\u754c\u3002 \u7531\u4e8e\u5fae\u5206\u65b9\u7a0b\u4e2d\u5f80\u5f80\u4f1a\u7528\u5230\u5f88\u591a\u590d\u53d8\u51fd\u6570\u7684\u77e5\u8bc6\uff0c\u6240\u4ee5\u5927\u5bb6\u53ef\u4ee5\u53c2\u8003 MIT18.04: Complex variables functions \u7684\u8bfe\u7a0b notes \u6765\u8865\u9f50\u5148\u4fee\u77e5\u8bc6\u3002 MIT18.03: differential equations ) \u4e3b\u8981\u8986\u76d6\u4e86\u5e38\u5fae\u5206\u65b9\u7a0b\u7684\u6c42\u89e3\uff0c\u5728\u6b64\u57fa\u7840\u4e4b\u4e0a MIT18.152: Partial differential equations ) 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Since there's no universally perfect tool, there's no universally perfect language.","title":"\u7f16\u7a0b\u5165\u95e8"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#shell","text":"MIT-Missing-Semester","title":"Shell"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#python","text":"Harvard CS50: This is CS50x UCB CS61A: Structure and Interpretation of Computer Programs","title":"Python"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#c","text":"Stanford CS106B/X: Programming Abstractions Stanford CS106L: Standard C++ Programming","title":"C++"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#rust","text":"Stanford CS110L: Safety in Systems Programming","title":"Rust"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#ocaml","text":"Cornell CS3110 textbook: Functional Programming in OCaml","title":"OCaml"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_20","text":"","title":"\u7535\u5b50\u57fa\u7840"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_21","text":"\u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u4e86\u89e3\u4e00\u4e9b\u57fa\u7840\u7684\u7535\u8def\u77e5\u8bc6\uff0c\u611f\u53d7\u4ece\u4f20\u611f\u5668\u6536\u96c6\u6570\u636e\u5230\u6570\u636e\u5206\u6790\u518d\u5230\u7b97\u6cd5\u9884\u6d4b\u6574\u6761\u6d41\u6c34\u7ebf\uff0c\u5bf9\u4e8e\u540e\u7eed\u77e5\u8bc6\u7684\u5b66\u4e60\u4ee5\u53ca\u8ba1\u7b97\u601d\u7ef4\u7684\u57f9\u517b\u8fd8\u662f\u5f88\u6709\u5e2e\u52a9\u7684\u3002 EE16A&B: Designing Information Devices and Systems I&II \u662f\u4f2f\u514b\u5229 EE \u5b66\u751f\u7684\u5927\u4e00\u5165\u95e8\u8bfe\uff0c\u5176\u4e2d EE16A \u6ce8\u91cd\u901a\u8fc7\u7535\u8def\u4ece\u5b9e\u9645\u73af\u5883\u4e2d\u6536\u96c6\u548c\u5206\u6790\u6570\u636e\uff0c\u800c EE16B \u5219\u4fa7\u91cd\u4ece\u8fd9\u4e9b\u6536\u96c6\u5230\u7684\u6570\u636e\u8fdb\u884c\u5206\u6790\u5e76\u505a\u51fa\u9884\u6d4b\u884c\u4e3a\u3002","title":"\u7535\u8def\u57fa\u7840"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_22","text":"\u4fe1\u53f7\u4e0e\u7cfb\u7edf\u662f\u4e00\u95e8\u6211\u89c9\u5f97\u975e\u5e38\u503c\u5f97\u4e00\u4e0a\u7684\u8bfe\uff0c\u6700\u521d\u5b66\u5b83\u53ea\u662f\u4e3a\u4e86\u6ee1\u8db3\u6211\u5bf9\u5085\u91cc\u53f6\u53d8\u6362\u7684\u597d\u5947\uff0c\u4f46\u5b66\u5b8c\u4e4b\u540e\u6211\u624d\u4e0d\u7981\u611f\u53f9\uff0c\u5085\u7acb\u53f6\u53d8\u6362\u7ed9\u6211\u63d0\u4f9b\u4e86\u4e00\u4e2a\u5168\u65b0\u7684\u89c6\u89d2\u53bb\u770b\u5f85\u8fd9\u4e2a\u4e16\u754c\uff0c\u5c31\u5982\u540c\u5fae\u5206\u65b9\u7a0b\u4e00\u6837\uff0c\u8ba9\u4f60\u6c89\u6d78\u5728\u7528\u6570\u5b66\u53bb\u7cbe\u786e\u63cf\u7ed8\u548c\u523b\u753b\u8fd9\u4e2a\u4e16\u754c\u7684\u4f18\u96c5\u4e0e\u795e\u5947\u4e4b\u4e2d\u3002 MIT 6.003: signal and systems 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Algorithms and Intractable Problems","title":"\u7b97\u6cd5\u8bbe\u8ba1\u4e0e\u5206\u6790"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_26","text":"","title":"\u8f6f\u4ef6\u5de5\u7a0b"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_27","text":"\u4e00\u4efd\u201c\u80fd\u8dd1\u201d\u7684\u4ee3\u7801\uff0c\u548c\u4e00\u4efd\u9ad8\u8d28\u91cf\u7684\u5de5\u4e1a\u7ea7\u4ee3\u7801\u662f\u6709\u672c\u8d28\u533a\u522b\u7684\u3002\u56e0\u6b64\u6211\u975e\u5e38\u63a8\u8350\u4f4e\u5e74\u7ea7\u7684\u540c\u5b66\u5b66\u4e60\u4e00\u4e0b MIT 6.031: Software Construction \u8fd9\u95e8\u8bfe\uff0c\u5b83\u4f1a\u4ee5 Java \u8bed\u8a00\u4e3a\u57fa\u7840\uff0c\u4ee5\u4e30\u5bcc\u7ec6\u81f4\u7684\u9605\u8bfb\u6750\u6599\u548c\u7cbe\u5fc3\u8bbe\u8ba1\u7684\u7f16\u7a0b\u7ec3\u4e60\u4f20\u6388\u5982\u4f55\u7f16\u5199 \u4e0d\u6613\u51fa bug\u3001\u7b80\u660e\u6613\u61c2\u3001\u6613\u4e8e\u7ef4\u62a4\u4fee\u6539 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2021\u5e7412\u670812\u65e5\u5199\u4e8e\u71d5\u56ed","title":"\u540e\u8bb0"},{"location":"en/%E5%9F%B9%E5%85%BB%E6%96%B9%E6%A1%88Pro/","text":"under construction.","title":"\u57f9\u517b\u65b9\u6848Pro"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/","text":"\u597d\u4e66\u63a8\u8350 \u7531\u4e8e\u7248\u6743\u539f\u56e0\uff0c\u4e0b\u9762\u5217\u4e3e\u7684\u56fe\u4e66\u4e2d\u9664\u4e86\u5f00\u6e90\u8d44\u6e90\u63d0\u4f9b\u4e86\u94fe\u63a5\uff0c\u5176\u4ed6\u7684\u8d44\u6e90\u8bf7\u5927\u5bb6\u81ea\u884c\u901a\u8fc7 libgen \u6216 z-lib \u67e5\u627e\u3002 \u8d44\u6e90\u6c47\u603b Free Programming Books : \u5f00\u6e90\u7f16\u7a0b\u4e66\u7c4d\u8d44\u6e90\u6c47\u603b CS Textbook Recommendations : \u8ba1\u7b97\u673a\u79d1\u5b66\u65b9\u5411\u63a8\u8350\u6559\u6750\u5217\u8868 C Book Guide and List : C\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 C++ Book Guide and List : C++\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 Python Book Guide and List : Python\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 \u7cfb\u7edf\u5165\u95e8 Computer Systems: A Programmer's Perspective [ \u8c46\u74e3 ] Principles of Computer System Design: An Introduction [ \u8c46\u74e3 ] \u64cd\u4f5c\u7cfb\u7edf \u73b0\u4ee3\u64cd\u4f5c\u7cfb\u7edf: \u539f\u7406\u4e0e\u5b9e\u73b0 [ \u8c46\u74e3 ] Operating Systems: Three Easy Pieces [ \u8c46\u74e3 ] Modern Operating Systems [ \u8c46\u74e3 ] Operating Systems: Principles and Practice [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u7f51\u7edc Computer Networks: A Systems Approach [ \u8c46\u74e3 ] Computer Networking: A Top-Down Approach [ \u8c46\u74e3 ] \u5206\u5e03\u5f0f\u7cfb\u7edf Patterns of Distributed System (Blog) Distributed Systems for Fun and Profit (Blog) Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems [ \u8c46\u74e3 ] \u6570\u636e\u5e93\u7cfb\u7edf Architecture of a Database System [ \u8c46\u74e3 ] Readings in Database Systems [ \u8c46\u74e3 ] Database System Concepts [ \u8c46\u74e3 ] \u7f16\u8bd1\u539f\u7406 Engineering a Compiler [ \u8c46\u74e3 ] Compilers: Principles, Techniques, and Tools [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u7f16\u7a0b\u8bed\u8a00 \u8ba1\u7b97\u673a\u7a0b\u5e8f\u7684\u6784\u9020\u548c\u89e3\u91ca [ \u8c46\u74e3 ] Essentials of Programming Languages [ \u8c46\u74e3 ] Practical Foundations for Programming Languages [ \u8c46\u74e3 ] Software Foundations [ \u8c46\u74e3 ] [ \u5317\u5927\u76f8\u5173\u8bfe\u7a0b ] Types and Programming Languages [ \u8c46\u74e3 ] [ \u5317\u5927\u76f8\u5173\u8bfe\u7a0b ] \u4f53\u7cfb\u7ed3\u6784 \u8d85\u6807\u91cf\u5904\u7406\u5668\u8bbe\u8ba1: Superscalar RISC Processor Design [ \u8c46\u74e3 ] Computer Organization and Design RISC-V Edition [ \u8c46\u74e3 ] Computer Organization and Design: The Hardware/Software Interface [ \u8c46\u74e3 ] Computer Architecture: A Quantitative Approach [ \u8c46\u74e3 ] \u7406\u8bba\u8ba1\u7b97\u673a\u79d1\u5b66 Introduction to the Theory of Computation [ \u8c46\u74e3 ] \u5bc6\u7801\u5b66 Cryptography Engineering: Design Principles and Practical Applications [ \u8c46\u74e3 ] Introduction to Modern Cryptography [ \u8c46\u74e3 ] \u9006\u5411\u5de5\u7a0b \u9006\u5411\u5de5\u7a0b\u6838\u5fc3\u539f\u7406 [ \u8c46\u74e3 ] \u52a0\u5bc6\u4e0e\u89e3\u5bc6 [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u56fe\u5f62\u5b66 Monte Carlo theory, methods and examples Advanced Global Illumination [ \u8c46\u74e3 ] Fundamentals of Computer Graphics [ \u8c46\u74e3 ] Fluid Simulation for Computer Graphics [ \u8c46\u74e3 ] Physically Based Rendering: From Theory To Implementation [ \u8c46\u74e3 ] Real-Time Rendering [ \u8c46\u74e3 ] \u6e38\u620f\u5f15\u64ce \u6e38\u620f\u7f16\u7a0b\u6a21\u5f0f: Game Programming Patterns [ \u8c46\u74e3 ] \u5b9e\u65f6\u78b0\u649e\u68c0\u6d4b\u7b97\u6cd5\u6280\u672f [ \u8c46\u74e3 ] Game AI Pro Series [ \u8c46\u74e3 ] Artificial Intelligence for Games [ \u8c46\u74e3 ] Game Engine Architecture [ \u8c46\u74e3 ] Game Programming Gems Series [ \u8c46\u74e3 ] \u8f6f\u4ef6\u5de5\u7a0b Software Engineering at Google [ \u8c46\u74e3 ] \u8bbe\u8ba1\u6a21\u5f0f \u8bbe\u8ba1\u6a21\u5f0f: \u53ef\u590d\u7528\u9762\u5411\u5bf9\u8c61\u8f6f\u4ef6\u7684\u57fa\u7840 [ \u8c46\u74e3 ] \u5927\u8bdd\u8bbe\u8ba1\u6a21\u5f0f [ \u8c46\u74e3 ] Head First \u8bbe\u8ba1\u6a21\u5f0f [ \u8c46\u74e3 ] \u6df1\u5ea6\u5b66\u4e60 \u52a8\u624b\u5b66\u6df1\u5ea6\u5b66\u4e60 [ \u8c46\u74e3 ] \u795e\u7ecf\u7f51\u7edc\u4e0e\u6df1\u5ea6\u5b66\u4e60 [ \u8c46\u74e3 ] \u6df1\u5ea6\u5b66\u4e60\u5165\u95e8 [ \u8c46\u74e3 ] \u7b80\u5355\u7c97\u66b4 TensorFlow 2 (Tutorial) Speech and Language Processing [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u89c6\u89c9 Multiple View Geometry in Computer Vision [ \u8c46\u74e3 ] \u673a\u5668\u4eba Probabilistic Robotics [ \u8c46\u74e3 ] \u9762\u8bd5 \u5251\u6307 Offer\uff1a\u540d\u4f01\u9762\u8bd5\u5b98\u7cbe\u8bb2\u5178\u578b\u7f16\u7a0b\u9898 [ \u8c46\u74e3 ] Cracking The Coding Interview [ \u8c46\u74e3 ]","title":"Book Recommendation"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_1","text":"\u7531\u4e8e\u7248\u6743\u539f\u56e0\uff0c\u4e0b\u9762\u5217\u4e3e\u7684\u56fe\u4e66\u4e2d\u9664\u4e86\u5f00\u6e90\u8d44\u6e90\u63d0\u4f9b\u4e86\u94fe\u63a5\uff0c\u5176\u4ed6\u7684\u8d44\u6e90\u8bf7\u5927\u5bb6\u81ea\u884c\u901a\u8fc7 libgen \u6216 z-lib \u67e5\u627e\u3002","title":"\u597d\u4e66\u63a8\u8350"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_2","text":"Free Programming Books : \u5f00\u6e90\u7f16\u7a0b\u4e66\u7c4d\u8d44\u6e90\u6c47\u603b CS Textbook Recommendations : \u8ba1\u7b97\u673a\u79d1\u5b66\u65b9\u5411\u63a8\u8350\u6559\u6750\u5217\u8868 C Book Guide and List : C\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 C++ Book Guide and List : C++\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 Python Book Guide and List : Python\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_3","text":"Computer Systems: A Programmer's Perspective [ \u8c46\u74e3 ] Principles of Computer System Design: An Introduction [ \u8c46\u74e3 ]","title":"\u7cfb\u7edf\u5165\u95e8"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_4","text":"\u73b0\u4ee3\u64cd\u4f5c\u7cfb\u7edf: \u539f\u7406\u4e0e\u5b9e\u73b0 [ \u8c46\u74e3 ] Operating Systems: Three Easy Pieces [ \u8c46\u74e3 ] Modern Operating Systems [ \u8c46\u74e3 ] Operating Systems: Principles and Practice [ \u8c46\u74e3 ]","title":"\u64cd\u4f5c\u7cfb\u7edf"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_5","text":"Computer Networks: A Systems Approach [ \u8c46\u74e3 ] Computer Networking: A Top-Down Approach [ \u8c46\u74e3 ]","title":"\u8ba1\u7b97\u673a\u7f51\u7edc"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_6","text":"Patterns of Distributed System (Blog) Distributed Systems for Fun and Profit (Blog) Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems [ \u8c46\u74e3 ]","title":"\u5206\u5e03\u5f0f\u7cfb\u7edf"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_7","text":"Architecture of a Database System [ \u8c46\u74e3 ] Readings in Database Systems [ \u8c46\u74e3 ] Database System Concepts [ \u8c46\u74e3 ]","title":"\u6570\u636e\u5e93\u7cfb\u7edf"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_8","text":"Engineering a Compiler [ \u8c46\u74e3 ] Compilers: Principles, Techniques, and Tools [ \u8c46\u74e3 ]","title":"\u7f16\u8bd1\u539f\u7406"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_9","text":"\u8ba1\u7b97\u673a\u7a0b\u5e8f\u7684\u6784\u9020\u548c\u89e3\u91ca [ \u8c46\u74e3 ] Essentials of Programming Languages [ \u8c46\u74e3 ] Practical Foundations for Programming Languages [ \u8c46\u74e3 ] Software Foundations [ \u8c46\u74e3 ] [ 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\u73af\u5883\u51fa\u73b0\u4e86\u95ee\u9898\uff0c\u53ef\u4ee5\u8003\u8651\u4f7f\u7528 Overleaf \u8fd9\u4e2a\u5728\u7ebf LaTeX \u7f16\u8f91\u7f51\u7ad9\u3002\u7ad9\u5185\u4e0d\u4ec5\u6709\u5404\u79cd\u5404\u6837\u7684 LaTeX \u6a21\u7248\u4f9b\u4f60\u9009\u62e9\uff0c\u8fd8\u514d\u53bb\u4e86\u73af\u5883\u914d\u7f6e\u7684\u96be\u9898\u3002 \u9605\u8bfb\u4e0b\u9762\u4e09\u7bc7 Tutorial: Part-1 , Part-2 , Part-3 \u3002 \u5b66\u4e60 LaTeX \u6700\u597d\u7684\u65b9\u5f0f\u5f53\u7136\u662f\u5199\u8bba\u6587\uff0c\u4e0d\u8fc7\u4ece\u4e00\u95e8\u6570\u5b66\u8bfe\u5165\u624b\u7528 LaTeX \u5199\u4f5c\u4e1a\u4e5f\u662f\u4e00\u4e2a\u4e0d\u9519\u7684\u9009\u62e9\u3002","title":"\u5982\u4f55\u5b66\u4e60 LaTeX"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/","text":"Vim \u4e3a\u4ec0\u4e48\u5b66\u4e60 Vim \u5728\u6211\u770b\u6765 Vim \u7f16\u8f91\u5668\u6709\u5982\u4e0b\u7684\u597d\u5904\uff1a 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\u7684\u5b66\u4e60\u8d44\u6599\u6d69\u5982\u70df\u6d77\uff0c\u4f46\u638c\u63e1\u5b83\u6700\u597d\u7684\u65b9\u5f0f\u8fd8\u662f\u5c06\u5b83\u7528\u5728\u65e5\u5e38\u7684\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u800c\u4e0d\u662f\u4e00\u4e0a\u6765\u5c31\u53bb\u5b66\u5404\u79cd\u82b1\u91cc\u80e1\u54e8\u7684\u9ad8\u7ea7 Vim \u6280\u5de7\u3002\u4e2a\u4eba\u63a8\u8350\u7684\u5b66\u4e60\u8def\u7ebf\u5982\u4e0b\uff1a \u5148\u9605\u8bfb \u8fd9\u7bc7 tutorial \uff0c\u638c\u63e1\u57fa\u672c\u7684 Vim \u6982\u5ff5\u548c\u4f7f\u7528\u65b9\u5f0f\u3002 \u7528 Vim \u81ea\u5e26\u7684 vimtutor \u8fdb\u884c\u7ec3\u4e60\uff0c\u5b89\u88c5\u5b8c Vim \u4e4b\u540e\u76f4\u63a5\u5728\u547d\u4ee4\u884c\u91cc\u8f93\u5165 vimtutor \u5373\u53ef\u8fdb\u5165\u7ec3\u4e60\u7a0b\u5e8f\u3002 \u6700\u540e\u5c31\u662f\u5f3a\u8feb\u81ea\u5df1\u4f7f\u7528 Vim \u8fdb\u884c\u5f00\u53d1\uff0cIDE \u91cc\u53ef\u4ee5\u5b89\u88c5 Vim \u63d2\u4ef6\u3002 \u7b49\u4f60\u5b8c\u5168\u9002\u5e94 Vim \u4e4b\u540e\u65b0\u7684\u4e16\u754c\u4fbf\u5411\u4f60\u655e\u5f00\u4e86\u5927\u95e8\uff0c\u4f60\u53ef\u4ee5\u6309\u9700\u914d\u7f6e\u81ea\u5df1\u7684 Vim\uff08\u4fee\u6539 .vimrc \u6587\u4ef6\uff09\uff0c\u7f51\u4e0a\u6709\u6570\u4e0d\u80dc\u6570\u7684\u8d44\u6e90\u53ef\u4ee5\u501f\u9274\u3002 \u63a8\u8350\u53c2\u8003\u8d44\u6599 Neil, Drew. Practical Vim: Edit Text at the Speed of Thought. N.p., Pragmatic Bookshelf, 2015. Neil, Drew. Modern Vim: Craft Your Development Environment with Vim 8 and Neovim. United States, Pragmatic Bookshelf.","title":"Vim"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#vim","text":"","title":"Vim"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#vim_1","text":"\u5728\u6211\u770b\u6765 Vim \u7f16\u8f91\u5668\u6709\u5982\u4e0b\u7684\u597d\u5904\uff1a \u8ba9\u4f60\u7684\u6574\u4e2a\u5f00\u53d1\u8fc7\u7a0b\u624b\u6307\u4e0d\u9700\u8981\u79bb\u5f00\u952e\u76d8\uff0c\u800c\u4e14\u5149\u6807\u7684\u79fb\u52a8\u4e0d\u9700\u8981\u65b9\u5411\u952e\u4f7f\u5f97\u4f60\u7684\u624b\u6307\u4e00\u76f4\u5904\u5728\u6253\u5b57\u7684\u6700\u4f73\u4f4d\u7f6e\u3002 \u65b9\u4fbf\u7684\u6587\u4ef6\u5207\u6362\u4ee5\u53ca\u9762\u677f\u63a7\u5236\u53ef\u4ee5\u8ba9\u4f60\u540c\u65f6\u5f00\u53d1\u591a\u4efd\u6587\u4ef6\u751a\u81f3\u540c\u4e00\u4e2a\u6587\u4ef6\u7684\u4e0d\u540c\u4f4d\u7f6e\u3002 Vim \u7684\u5b8f\u64cd\u4f5c\u53ef\u4ee5\u6279\u91cf\u5316\u5904\u7406\u91cd\u590d\u64cd\u4f5c\uff08\u4f8b\u5982\u591a\u884c tab\uff0c\u6279\u91cf\u52a0\u53cc\u5f15\u53f7\u7b49\u7b49\uff09 Vim \u662f\u5f88\u591a\u670d\u52a1\u5668\u81ea\u5e26\u7684\u547d\u4ee4\u884c\u7f16\u8f91\u5668\uff0c\u5f53\u4f60\u901a\u8fc7 ssh \u8fde\u63a5\u8fdc\u7a0b\u670d\u52a1\u5668\u4e4b\u540e\uff0c\u7531\u4e8e\u6ca1\u6709\u56fe\u5f62\u754c\u9762\uff0c\u53ea\u80fd\u5728\u547d\u4ee4\u884c\u91cc\u8fdb\u884c\u5f00\u53d1\uff08\u5f53\u7136\u73b0\u5728\u5f88\u591a IDE \u5982 VS Code \u63d0\u4f9b\u4e86 ssh \u63d2\u4ef6\u53ef\u4ee5\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff09\u3002 \u5f02\u5e38\u4e30\u5bcc\u7684\u63d2\u4ef6\u751f\u6001\uff0c\u8ba9\u4f60\u62e5\u6709\u4e16\u754c\u4e0a\u6700\u82b1\u91cc\u80e1\u54e8\u7684\u547d\u4ee4\u884c\u7f16\u8f91\u5668\u3002","title":"\u4e3a\u4ec0\u4e48\u5b66\u4e60 Vim"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#vim_2","text":"\u4e0d\u5e78\u7684\u662f Vim \u7684\u5b66\u4e60\u66f2\u7ebf\u786e\u5b9e\u76f8\u5f53\u9661\u5ced\uff0c\u6211\u82b1\u4e86\u597d\u51e0\u4e2a\u661f\u671f\u624d\u6162\u6162\u9002\u5e94\u4e86\u7528 Vim \u8fdb\u884c\u5f00\u53d1\u7684\u8fc7\u7a0b\u3002\u6700\u5f00\u59cb\u4f60\u4f1a\u89c9\u5f97\u975e\u5e38\u4e0d\u9002\u5e94\uff0c\u4f46\u4e00\u65e6\u71ac\u8fc7\u4e86\u521d\u59cb\u9636\u6bb5\uff0c\u76f8\u4fe1\u6211\uff0c\u4f60\u4f1a\u7231\u4e0a Vim\u3002 Vim \u7684\u5b66\u4e60\u8d44\u6599\u6d69\u5982\u70df\u6d77\uff0c\u4f46\u638c\u63e1\u5b83\u6700\u597d\u7684\u65b9\u5f0f\u8fd8\u662f\u5c06\u5b83\u7528\u5728\u65e5\u5e38\u7684\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u800c\u4e0d\u662f\u4e00\u4e0a\u6765\u5c31\u53bb\u5b66\u5404\u79cd\u82b1\u91cc\u80e1\u54e8\u7684\u9ad8\u7ea7 Vim \u6280\u5de7\u3002\u4e2a\u4eba\u63a8\u8350\u7684\u5b66\u4e60\u8def\u7ebf\u5982\u4e0b\uff1a \u5148\u9605\u8bfb \u8fd9\u7bc7 tutorial \uff0c\u638c\u63e1\u57fa\u672c\u7684 Vim \u6982\u5ff5\u548c\u4f7f\u7528\u65b9\u5f0f\u3002 \u7528 Vim \u81ea\u5e26\u7684 vimtutor \u8fdb\u884c\u7ec3\u4e60\uff0c\u5b89\u88c5\u5b8c Vim \u4e4b\u540e\u76f4\u63a5\u5728\u547d\u4ee4\u884c\u91cc\u8f93\u5165 vimtutor \u5373\u53ef\u8fdb\u5165\u7ec3\u4e60\u7a0b\u5e8f\u3002 \u6700\u540e\u5c31\u662f\u5f3a\u8feb\u81ea\u5df1\u4f7f\u7528 Vim \u8fdb\u884c\u5f00\u53d1\uff0cIDE \u91cc\u53ef\u4ee5\u5b89\u88c5 Vim \u63d2\u4ef6\u3002 \u7b49\u4f60\u5b8c\u5168\u9002\u5e94 Vim \u4e4b\u540e\u65b0\u7684\u4e16\u754c\u4fbf\u5411\u4f60\u655e\u5f00\u4e86\u5927\u95e8\uff0c\u4f60\u53ef\u4ee5\u6309\u9700\u914d\u7f6e\u81ea\u5df1\u7684 Vim\uff08\u4fee\u6539 .vimrc \u6587\u4ef6\uff09\uff0c\u7f51\u4e0a\u6709\u6570\u4e0d\u80dc\u6570\u7684\u8d44\u6e90\u53ef\u4ee5\u501f\u9274\u3002","title":"\u5982\u4f55\u5b66\u4e60 Vim"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#_1","text":"Neil, Drew. Practical Vim: Edit Text at the Speed of Thought. N.p., Pragmatic Bookshelf, 2015. Neil, Drew. Modern Vim: Craft Your Development Environment with Vim 8 and Neovim. United States, Pragmatic Bookshelf.","title":"\u63a8\u8350\u53c2\u8003\u8d44\u6599"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/thesis/","text":"\u6bd5\u4e1a\u8bba\u6587 \u4e3a\u4ec0\u4e48\u5199\u8fd9\u4efd\u6559\u7a0b 2022\u5e74\uff0c\u6211\u672c\u79d1\u6bd5\u4e1a\u4e86\u3002\u5728\u5f00\u59cb\u52a8\u624b\u5199\u6bd5\u4e1a\u8bba\u6587\u7684\u65f6\u5019\uff0c\u6211\u5c34\u5c2c\u5730\u53d1\u73b0\uff0c\u6211\u5bf9 Word \u7684\u638c\u63e1\u7a0b\u5ea6\u4ec5\u9650\u4e8e\u8c03\u8282\u5b57\u4f53\u3001\u4fdd\u5b58\u5bfc\u51fa\u8fd9\u4e9b\u50bb\u74dc\u529f\u80fd\u3002\u66fe\u60f3\u8f6c\u6218 Latex\uff0c\u4f46\u8bba\u6587\u7684\u6bb5\u843d\u683c\u5f0f\u8981\u6c42\u8c03\u6574\u8d77\u6765\u8fd8\u662f\u7528 Word 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\u6cb9\u7ba1\u89c6\u9891\u4e0b\u8f7d\u5668\u3002 zlibrary : \u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9(\u53ef\u80fd\u9700\u8981\u7ffb\u5899)\u3002 \u8bbe\u8ba1\u5de5\u5177 excalidraw : \u4e00\u6b3e\u624b\u7ed8\u98ce\u683c\u7684\u7ed8\u56fe\u5de5\u5177\uff0c\u975e\u5e38\u9002\u5408\u7ed8\u5236\u8bfe\u7a0b\u62a5\u544a\u6216\u8005PPT\u5185\u7684\u793a\u610f\u56fe\u3002 origamiway : \u624b\u628a\u624b\u6559\u4f60\u600e\u4e48\u6298\u7eb8\u3002 thingiverse : \u56ca\u62ec\u5404\u7c7b 2D/3D \u8bbe\u8ba1\u8d44\u6e90\uff0c\u5176 STL \u6587\u4ef6\u4e0b\u8f7d\u53ef\u76f4\u63a5 3D \u6253\u5370\u3002 iconfont : \u56fd\u5185\u6700\u5927\u7684\u56fe\u6807\u548c\u63d2\u753b\u8d44\u6e90\u5e93\uff0c\u53ef\u7528\u4e8e\u5f00\u53d1\u6216\u7ed8\u5236\u7cfb\u7edf\u67b6\u6784\u56fe\u3002 turbosquid : \u53ef\u4ee5\u8d2d\u4e70\u5404\u5f0f\u5404\u6837\u7684\u6a21\u578b\u3002 \u7f16\u7a0b\u76f8\u5173 sqlfiddle : \u4e00\u4e2a\u7b80\u6613\u7684\u5728\u7ebf SQL Playground\u3002 godbolt : \u975e\u5e38\u65b9\u4fbf\u7684\u7f16\u8bd1\u5668\u63a2\u7d22\u5de5\u5177\u3002\u4f60\u53ef\u4ee5\u5199\u4e00\u6bb5 C/C++ \u4ee3\u7801\uff0c\u9009\u62e9\u4e00\u6b3e\u7f16\u8bd1\u5668\uff0c\u7136\u540e\u4fbf\u53ef\u4ee5\u89c2\u5bdf\u751f\u6210\u7684\u5177\u4f53\u6c47\u7f16\u4ee3\u7801\u3002 explainshell : \u4f60\u662f\u5426\u66fe\u4e3a\u4e00\u6bb5 shell \u4ee3\u7801\u7684\u5177\u4f53\u542b\u4e49\u611f\u5230\u56f0\u6270\uff1fmanpage \u770b\u534a\u5929\u8fd8\u662f\u4e0d\u660e\u6240\u4ee5\uff1f\u8bd5\u8bd5\u8fd9\u4e2a\u7f51\u7ad9\uff01 regex101 : \u6b63\u5219\u8868\u8fbe\u5f0f\u8c03\u8bd5\u7f51\u7ad9\uff0c\u652f\u6301\u5404\u79cd\u7f16\u7a0b\u8bed\u8a00\u7684\u5339\u914d\u6807\u51c6\u3002 typingtom : \u9488\u5bf9\u7a0b\u5e8f\u5458\u7684\u6253\u5b57\u7ec3\u4e60/\u6d4b\u901f\u7f51\u7ad9\u3002 \u5b66\u4e60\u7f51\u7ad9 HFS : \u5404\u7c7b\u8f6f\u4ef6\u6559\u7a0b\u3002 os-wiki : \u64cd\u4f5c\u7cfb\u7edf\u6280\u672f\u8d44\u6e90\u767e\u79d1\u5168\u4e66\u3002 Shadertoy : \u7f16\u5199\u5404\u5f0f\u5404\u6837\u7684 shader\u3002 \u6742\u9879 tophub : \u65b0\u95fb\u70ed\u699c\u5408\u96c6\uff08\u7efc\u5408\u4e86\u77e5\u4e4e\u3001\u5fae\u535a\u3001\u767e\u5ea6\u3001\u5fae\u4fe1\u7b49\uff09\u3002 speedtest : \u5728\u7ebf\u7f51\u7edc\u6d4b\u901f\u7f51\u7ad9\u3002 public-apis : \u516c\u5171 API \u5408\u96c6\u5217\u8868\u3002","title":"\u5b9e\u7528\u5de5\u5177\u7bb1"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_1","text":"","title":"\u5b9e\u7528\u5de5\u5177\u7bb1"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_2","text":"Libgen : PDF\u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9\u3002 z-epub : ePub\u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9\u3002 bitdownloader : \u6cb9\u7ba1\u89c6\u9891\u4e0b\u8f7d\u5668\u3002 zlibrary : \u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9(\u53ef\u80fd\u9700\u8981\u7ffb\u5899)\u3002","title":"\u4e0b\u8f7d\u5de5\u5177"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_3","text":"excalidraw : \u4e00\u6b3e\u624b\u7ed8\u98ce\u683c\u7684\u7ed8\u56fe\u5de5\u5177\uff0c\u975e\u5e38\u9002\u5408\u7ed8\u5236\u8bfe\u7a0b\u62a5\u544a\u6216\u8005PPT\u5185\u7684\u793a\u610f\u56fe\u3002 origamiway : \u624b\u628a\u624b\u6559\u4f60\u600e\u4e48\u6298\u7eb8\u3002 thingiverse : \u56ca\u62ec\u5404\u7c7b 2D/3D \u8bbe\u8ba1\u8d44\u6e90\uff0c\u5176 STL \u6587\u4ef6\u4e0b\u8f7d\u53ef\u76f4\u63a5 3D \u6253\u5370\u3002 iconfont : \u56fd\u5185\u6700\u5927\u7684\u56fe\u6807\u548c\u63d2\u753b\u8d44\u6e90\u5e93\uff0c\u53ef\u7528\u4e8e\u5f00\u53d1\u6216\u7ed8\u5236\u7cfb\u7edf\u67b6\u6784\u56fe\u3002 turbosquid : \u53ef\u4ee5\u8d2d\u4e70\u5404\u5f0f\u5404\u6837\u7684\u6a21\u578b\u3002","title":"\u8bbe\u8ba1\u5de5\u5177"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_4","text":"sqlfiddle : \u4e00\u4e2a\u7b80\u6613\u7684\u5728\u7ebf SQL Playground\u3002 godbolt : \u975e\u5e38\u65b9\u4fbf\u7684\u7f16\u8bd1\u5668\u63a2\u7d22\u5de5\u5177\u3002\u4f60\u53ef\u4ee5\u5199\u4e00\u6bb5 C/C++ \u4ee3\u7801\uff0c\u9009\u62e9\u4e00\u6b3e\u7f16\u8bd1\u5668\uff0c\u7136\u540e\u4fbf\u53ef\u4ee5\u89c2\u5bdf\u751f\u6210\u7684\u5177\u4f53\u6c47\u7f16\u4ee3\u7801\u3002 explainshell : \u4f60\u662f\u5426\u66fe\u4e3a\u4e00\u6bb5 shell \u4ee3\u7801\u7684\u5177\u4f53\u542b\u4e49\u611f\u5230\u56f0\u6270\uff1fmanpage \u770b\u534a\u5929\u8fd8\u662f\u4e0d\u660e\u6240\u4ee5\uff1f\u8bd5\u8bd5\u8fd9\u4e2a\u7f51\u7ad9\uff01 regex101 : \u6b63\u5219\u8868\u8fbe\u5f0f\u8c03\u8bd5\u7f51\u7ad9\uff0c\u652f\u6301\u5404\u79cd\u7f16\u7a0b\u8bed\u8a00\u7684\u5339\u914d\u6807\u51c6\u3002 typingtom : \u9488\u5bf9\u7a0b\u5e8f\u5458\u7684\u6253\u5b57\u7ec3\u4e60/\u6d4b\u901f\u7f51\u7ad9\u3002","title":"\u7f16\u7a0b\u76f8\u5173"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_5","text":"HFS : \u5404\u7c7b\u8f6f\u4ef6\u6559\u7a0b\u3002 os-wiki : \u64cd\u4f5c\u7cfb\u7edf\u6280\u672f\u8d44\u6e90\u767e\u79d1\u5168\u4e66\u3002 Shadertoy : \u7f16\u5199\u5404\u5f0f\u5404\u6837\u7684 shader\u3002","title":"\u5b66\u4e60\u7f51\u7ad9"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_6","text":"tophub : \u65b0\u95fb\u70ed\u699c\u5408\u96c6\uff08\u7efc\u5408\u4e86\u77e5\u4e4e\u3001\u5fae\u535a\u3001\u767e\u5ea6\u3001\u5fae\u4fe1\u7b49\uff09\u3002 speedtest : \u5728\u7ebf\u7f51\u7edc\u6d4b\u901f\u7f51\u7ad9\u3002 public-apis : \u516c\u5171 API \u5408\u96c6\u5217\u8868\u3002","title":"\u6742\u9879"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/%E7%BF%BB%E5%A2%99/","text":"\u7ffb\u5899 \u6b64\u94fe\u63a5 \u51fa\u73b0\u5728\u8fd9\u91cc\u7eaf\u5c5e\u4e8c\u8fdb\u5236 bit \u7684\u968f\u610f\u7ec4\u5408\uff0c\u4e0e\u672c\u4eba\u6beb\u65e0\u5173\u7cfb\u3002","title":"\u7ffb\u5899"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/%E7%BF%BB%E5%A2%99/#_1","text":"\u6b64\u94fe\u63a5 \u51fa\u73b0\u5728\u8fd9\u91cc\u7eaf\u5c5e\u4e8c\u8fdb\u5236 bit \u7684\u968f\u610f\u7ec4\u5408\uff0c\u4e0e\u672c\u4eba\u6beb\u65e0\u5173\u7cfb\u3002","title":"\u7ffb\u5899"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/CS162/","text":"CS162: Operating System \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A, CS61B, CS61C \u7f16\u7a0b\u8bed\u8a00\uff1aC, x86\u6c47\u7f16 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a200 \u5c0f\u65f6+\uff0c\u4e0a\u4e0d\u5c01\u9876 \u8fd9\u95e8\u8bfe\u8ba9\u6211\u8bb0\u5fc6\u72b9\u65b0\u7684\u6709\u4e24\u4e2a\u90e8\u5206\uff1a \u9996\u5148\u662f\u6559\u6750\uff0c\u8fd9\u672c\u4e66\u7528\u7684\u6559\u6750 Operating Systems: Principles and Practice (2nd Edition) \u4e00\u5171\u56db\u5377\uff0c\u5199\u5f97\u975e\u5e38\u6df1\u5165\u6d45\u51fa\uff0c\u5f88\u597d\u5730\u5f25\u8865\u4e86 MIT6.S081 \u5728\u7406\u8bba\u77e5\u8bc6\u4e0a\u7684\u4e9b\u8bb8\u7a7a\u767d\uff0c\u975e\u5e38\u5efa\u8bae\u5927\u5bb6\u9605\u8bfb\u3002\u76f8\u5173\u8d44\u6e90\u4f1a\u5206\u4eab\u5728\u672c\u4e66\u7684\u7ecf\u5178\u4e66\u7c4d\u63a8\u8350\u6a21\u5757\u3002 \u5176\u6b21\u662f\u8fd9\u95e8\u8bfe\u7684 Project \u2014\u2014 Pintos\u3002Pintos \u662f\u7531 Ben Pfaff \u7b49\u4eba\u5728 x86 \u5e73\u53f0\u4e0a\u7f16\u5199\u7684\u6559\u5b66\u7528\u64cd\u4f5c\u7cfb\u7edf\uff0cBen Pfaff \u751a\u81f3\u4e13\u95e8\u53d1\u4e86\u7bc7 paper \u6765\u9610\u8ff0 Pintos \u7684\u8bbe\u8ba1\u601d\u60f3\u3002 \u548c MIT \u7684 xv6 \u5c0f\u800c\u7cbe\u7684 lab \u8bbe\u8ba1\u7406\u5ff5\u4e0d\u540c\uff0cPintos \u66f4\u6ce8\u91cd\u7cfb\u7edf\u7684 Design and Implementation\u3002Pintos \u672c\u8eab\u4ec5\u4e00\u4e07\u884c\u5de6\u53f3\uff0c\u53ea\u63d0\u4f9b\u4e86\u64cd\u4f5c\u7cfb\u7edf\u6700\u57fa\u672c\u7684\u529f\u80fd\u3002\u800c 4 \u4e2aProject\uff0c\u5c31\u662f\u8ba9\u4f60\u5728\u8fd9\u4e2a\u6781\u4e3a\u7cbe\u7b80\u7684\u64cd\u4f5c\u7cfb\u7edf\u4e4b\u4e0a\uff0c\u5206\u522b\u4e3a\u5176\u589e\u52a0\u7ebf\u7a0b\u8c03\u5ea6\u673a\u5236 (Project1)\uff0c\u7cfb\u7edf\u8c03\u7528 (Project2)\uff0c\u865a\u62df\u5185\u5b58 (Project3) \u4ee5\u53ca\u6587\u4ef6\u7cfb\u7edf (Project4)\u3002\u6240\u6709\u7684 Project \u90fd\u7ed9\u5b66\u751f\u7559\u6709\u5f88\u5927\u7684\u8bbe\u8ba1\u7a7a\u95f4\uff0c\u603b\u4ee3\u7801\u91cf\u5728 2000 \u884c\u5de6\u53f3\u3002\u6839\u636e Stanford \u5b66\u751f \u81ea\u5df1\u7684\u53cd\u9988 \uff0c\u5728 3-4 \u4eba\u7ec4\u961f\u7684\u60c5\u51b5\u4e0b\uff0c\u540e\u4e24\u4e2a Project \u7684\u4eba\u5747\u8017\u65f6\u4e5f\u5728 40 \u4e2a\u5c0f\u65f6\u4ee5\u4e0a\u3002 \u867d\u7136\u96be\u5ea6\u5f88\u5927\uff0c\u4f46 Stanford, Berkeley, JHU \u7b49\u591a\u6240\u7f8e\u56fd\u9876\u5c16\u540d\u6821\u7684\u64cd\u7edf\u8bfe\u7a0b\u5747\u91c7\u7528\u4e86 Pintos\u3002\u5982\u679c\u4f60\u771f\u7684\u5bf9\u64cd\u4f5c\u7cfb\u7edf\u5f88\u611f\u5174\u8da3\uff0cPintos \u4f1a\u6781\u5927\u5730\u63d0\u9ad8\u4f60\u7f16\u5199\u548c debug \u5e95\u5c42\u7cfb\u7edf\u4ee3\u7801\u7684\u80fd\u529b\u3002\u5728\u672c\u79d1\u9636\u6bb5\uff0c\u80fd\u81ea\u5df1\u8bbe\u8ba1\u3001\u5b9e\u73b0\u5e76 debug \u4e00\u4e2a\u5927\u578b\u7cfb\u7edf\uff0c\u662f\u4e00\u6bb5\u975e\u5e38\u73cd\u8d35\u7684\u7ecf\u5386\u3002 \u5317\u5927 2022 \u5e74\u6625\u5b63\u5b66\u671f\u7684\u64cd\u4f5c\u7cfb\u7edf\u5b9e\u9a8c\u73ed\u4e5f\u5c06\u4f1a\u9996\u6b21\u5f15\u5165 Pintos \u4f5c\u4e3a\u8bfe\u7a0b Project\u3002\u6211\u548c\u8be5\u8bfe\u7a0b\u7684 \u53e6\u4e00\u4f4d\u52a9\u6559 \u6574\u7406\u5e76\u5b8c\u5584\u4e86 Pintos \u7684 \u5b9e\u9a8c\u6587\u6863 \uff0c\u5e76\u5229\u7528 Docker 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\u8003\u6838\u4e4b\u4f59\u7684\u7528\u7231\u53d1\u7535\uff0c\u624d\u8ba9\u65e0\u6570\u5b66\u5b50\u6536\u83b7\u4e86\u96be\u5fd8\u7684\u672c\u79d1\u751f\u6daf\u3002\u4e5f\u671f\u5f85\u56fd\u5185\u80fd\u6709\u66f4\u591a\u8fd9\u6837\u7684\u826f\u5fc3\u597d\u8bfe\uff0c\u6211\u4e5f\u4f1a\u7b2c\u4e00\u65f6\u95f4\u6536\u5f55\u8fdb\u672c\u4e66\u4e2d\u8ba9\u66f4\u591a\u4eba\u53d7\u76ca\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/NJUOS/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://jyywiki.cn/OS/2022/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://space.bilibili.com/202224425/channel/collectiondetail?sid=192498 \u8bfe\u7a0b\u6559\u6750\uff1a http://pages.cs.wisc.edu/~remzi/OSTEP/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://jyywiki.cn/OS/2022/","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/NJUOS/#_3","text":"\u6309\u848b\u8001\u5e08\u7684\u8981\u6c42\uff0c\u6211\u7684\u4f5c\u4e1a\u5b9e\u73b0\u6ca1\u6709\u5f00\u6e90\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITLA/","text":"MIT18.06: Linear Algebra Descriptions Offered by: MIT Prerequisites: English Programming languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: Varying from person to person Gilbert Strang, a great mathematician at MIT, still insists on teaching in his eighties. His classic text book Introduction to Linear Algebra has been adopted as an official textbook by Tsinghua University. After reading the PDF version, I felt deeply guilty and spent more than 200 yuan to purchase a genuine version in English as collection. The cover of this book is attached below. If you can fully understand the mathematical meaning of the cover picture, then your understanding of linear algebra will definitely reach a new height. In addition to the course materials, the famous Youtuber 3Blue1Brown 's video series The Essence of Linear Algebra are also great learning resourses. Resources Course Website: https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/syllabus/ Recordings: refer to course website Textbook: Introduction to Linear Algebra, Gilbert Strang Assignments: refer to course website","title":"MIT18.06: Linear Algebra"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITLA/#mit1806-linear-algebra","text":"","title":"MIT18.06: Linear Algebra"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITLA/#descriptions","text":"Offered by: MIT Prerequisites: English Programming languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: Varying from person to person Gilbert Strang, a great mathematician at MIT, still insists on teaching in his eighties. His classic text book Introduction to Linear Algebra has been adopted as an official textbook by Tsinghua University. After reading the PDF version, I felt deeply guilty and spent more than 200 yuan to purchase a genuine version in English as collection. The cover of this book is attached below. If you can fully understand the mathematical meaning of the cover picture, then your understanding of linear algebra will definitely reach a new height. In addition to the course materials, the famous Youtuber 3Blue1Brown 's video series The Essence of Linear Algebra are also great learning resourses.","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITLA/#resources","text":"Course Website: https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/syllabus/ Recordings: refer to course website Textbook: Introduction to Linear Algebra, Gilbert Strang Assignments: refer to course website","title":"Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/","text":"MIT Calculus Course Descriptions Offered by: MIT Prerequisites: English Programming Languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: Varying from person to person The calculus course at MIT consists of MIT18.01: Single Variable Calculus and MIT18.02: Multivariable Calculus. If you are confident in your math, you can just read the course notes, which are written in a very simple and vivid way, so that you will not be tired of doing homework but can really see the essence of calculus. In addition to the course materials, the famous Youtuber 3Blue1Brown 's video series The Essence of Calculus are also great learning resources. Course Resources Course Website: 18.01 , 18.02 Recordings: refer to course website Textbook: refer to course website Assignments: refer to course website","title":"MIT18.01/18.02: Calculus"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/#mit-calculus-course","text":"","title":"MIT Calculus Course"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/#descriptions","text":"Offered by: MIT Prerequisites: English Programming Languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: Varying from person to person The calculus course at MIT consists of MIT18.01: Single Variable Calculus and MIT18.02: Multivariable Calculus. If you are confident in your math, you can just read the course notes, which are written in a very simple and vivid way, so that you will not be tired of doing homework but can really see the essence of calculus. In addition to the course materials, the famous Youtuber 3Blue1Brown 's video series The Essence of Calculus are also great learning resources.","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/#course-resources","text":"Course Website: 18.01 , 18.02 Recordings: refer to course website Textbook: refer to course website Assignments: refer to course website","title":"Course Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/information/","text":"MIT6.050J: Information theory and Entropy Descriptions Offered by: MIT Prerequisites: None Programming Languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours This is MIT's introductory information theory course for freshmen, Professor Penfield has written a special textbook for this course as course notes, which is in-depth and interesting. Course Resources Course Website: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/index.htm Textbook: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/syllabus/MIT6_050JS08_textbook.pdf Assignments: see the course website for details, including written assignments and Matlab programming assignments.","title":"MIT6.050J: Information theory and Entropy"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/information/#mit6050j-information-theory-and-entropy","text":"","title":"MIT6.050J: Information theory and Entropy"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/information/#descriptions","text":"Offered by: MIT Prerequisites: None Programming Languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours This is MIT's introductory information theory course for freshmen, Professor Penfield has written a special textbook for this course as course notes, which is in-depth and interesting.","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/information/#course-resources","text":"Course Website: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/index.htm Textbook: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/syllabus/MIT6_050JS08_textbook.pdf Assignments: see the course website for details, including written assignments and Matlab programming assignments.","title":"Course Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/6.042J/","text":"MIT 6.042J: Mathematics for Computer Science Descriptions Offered by: MIT Prerequisites: Calculus, Linear Algebra Programming Languages: Python preferred Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 50-70 hours This is MIT\u2018s discrete mathematics and probability course taught by the notable Tom Leighton (co-founder of Akamai). It is very useful for learning algorithms subsequently. Course Resources Course Website: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/ Recordings: https://www.youtube.com/playlist?list=PLB7540DEDD482705B Assignments: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/assignments/","title":"MIT 6.042J: Mathematics for Computer Science"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/6.042J/#mit-6042j-mathematics-for-computer-science","text":"","title":"MIT 6.042J: Mathematics for Computer Science"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/6.042J/#descriptions","text":"Offered by: MIT Prerequisites: Calculus, Linear Algebra Programming Languages: Python preferred Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 50-70 hours This is MIT\u2018s discrete mathematics and probability course taught by the notable Tom Leighton (co-founder of Akamai). It is very useful for learning algorithms subsequently.","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/6.042J/#course-resources","text":"Course Website: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/ Recordings: https://www.youtube.com/playlist?list=PLB7540DEDD482705B Assignments: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/assignments/","title":"Course Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS126/","text":"UCB CS126 : Probability theory Descriptions Offered by: UC Berkeley Prerequisites: CS70, Calculus, Linear Algebra Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours This is Berkeley's advanced probability course, which involves relatively advanced theoretical content such as statistics and stochastic processes, so a solid mathematical foundation is required. But as long as you stick with it you will certainly take your mastery of probability theory to a new level. The course is designed by Professor Jean Walrand, who has written an accompanying textbook, Probability in Electrical Engineering and Computer Science , in which each chapter uses a specific algorithm as a practical example to demonstrate the application of theory in practice. Such as PageRank, Route Planing, Speech Recognition, etc. The book is open source and can be downloaded as a free PDF or Epub version. Jean Walrand has also created accompanying Python implementations of the examples throughout the book, which are published online as Jupyter Notebook that readers can modify, debug and run them online interactively. In addition to the Homework, nine Labs will allow you to use probability theory to solve practical problems in Python. Course Resources Course Website: https://inst.eecs.berkeley.edu/~ee126/fa20/content.html Textbook: PDF , Epub , Jupyter Notebook Assignments: refer to the course website. Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPic/EECS126 - GitHub","title":"UCB CS126: probability theory"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS126/#ucb-cs126-probability-theory","text":"","title":"UCB CS126 : Probability theory"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS126/#descriptions","text":"Offered by: UC Berkeley Prerequisites: CS70, Calculus, Linear Algebra Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours This is Berkeley's advanced probability course, which involves relatively advanced theoretical content such as statistics and stochastic processes, so a solid mathematical foundation is required. But as long as you stick with it you will certainly take your mastery of probability theory to a new level. The course is designed by Professor Jean Walrand, who has written an accompanying textbook, Probability in Electrical Engineering and Computer Science , in which each chapter uses a specific algorithm as a practical example to demonstrate the application of theory in practice. Such as PageRank, Route Planing, Speech Recognition, etc. The book is open source and can be downloaded as a free PDF or Epub version. Jean Walrand has also created accompanying Python implementations of the examples throughout the book, which are published online as Jupyter Notebook that readers can modify, debug and run them online interactively. In addition to the Homework, nine Labs will allow you to use probability theory to solve practical problems in Python.","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS126/#course-resources","text":"Course Website: https://inst.eecs.berkeley.edu/~ee126/fa20/content.html Textbook: PDF , Epub , Jupyter Notebook Assignments: refer to the course website.","title":"Course Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS126/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPic/EECS126 - GitHub","title":"Personal Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS70/","text":"UCB CS70: Discrete Math and Probability Theory Descriptions Offered by: UC Berkeley Prerequisites: None Programming Languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 60 hours This is Berkeley's introductory discrete mathematics course. The biggest highlight of this course is that it not only teaches you theoretical knowledge, but also introduce the applications of theoretical knowledge in practical algorithms in each module. In this way, students majoring in CS can understand the essence of theoretical knowledge and use it in practice rather than struggle with cold formal mathematical symbols. Specific theory-algorithm correspondences are listed below. Logic proof: stable matching algorithm Graph theory: network topology design Basic number theory: RSA algorithm Polynomial ring: error-correcting code design Probability theory: Hash table design, load balancing, etc. The course notes are also written in a very in-depth manner, with derivations of formulas and practical examples, providing a good reading experience. Course Resources Course Website: http://www.eecs70.org/ Textbook: refer to course website Assignments: refer to course website Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/UCB-CS70 - GitHub","title":"UCB CS70: discrete Math and probability theory"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS70/#ucb-cs70-discrete-math-and-probability-theory","text":"","title":"UCB CS70: Discrete Math and Probability Theory"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS70/#descriptions","text":"Offered by: UC Berkeley Prerequisites: None Programming Languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 60 hours This is Berkeley's introductory discrete mathematics course. The biggest highlight of this course is that it not only teaches you theoretical knowledge, but also introduce the applications of theoretical knowledge in practical algorithms in each module. In this way, students majoring in CS can understand the essence of theoretical knowledge and use it in practice rather than struggle with cold formal mathematical symbols. Specific theory-algorithm correspondences are listed below. Logic proof: stable matching algorithm Graph theory: network topology design Basic number theory: RSA algorithm Polynomial ring: error-correcting code design Probability theory: Hash table design, load balancing, etc. The course notes are also written in a very in-depth manner, with derivations of formulas and practical examples, providing a good reading experience.","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS70/#course-resources","text":"Course Website: http://www.eecs70.org/ Textbook: refer to course website Assignments: refer to course website","title":"Course Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS70/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/UCB-CS70 - GitHub","title":"Personal Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/","text":"The Information Theory, Patter Recognition, and Neural Networks Descriptions Offered by: Cambridge Prerequisites: Calculus, Linear Algebra, Probabilities and Statistics Programming Languages: Anything would be OK, Python preferred Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 30-50 hours This is a course on information theory taught by Sir David MacKay at the University of Cambridge. The professor is a very famous scholar in information theory and neural networks, and the textbook for the course is a classic work in the field of information theory. Unfortunately, those whom God loves die young ... Course Resources Course Website: http://www.inference.org.uk/mackay/itila/ Recordings: https://www.youtube.com/playlist?list=PLruBu5BI5n4aFpG32iMbdWoRVAA-Vcso6 Textbooks: Information Theory, Inference, and Learning Algorithms Assignments: At the end of each lesson video, there are post-lesson exercises from the textbook R.I.P Prof. David MacKay","title":"The Information Theory, Patter Recognition, and Neural Networks"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/#the-information-theory-patter-recognition-and-neural-networks","text":"","title":"The Information Theory, Patter Recognition, and Neural Networks"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/#descriptions","text":"Offered by: Cambridge Prerequisites: Calculus, Linear Algebra, Probabilities and Statistics Programming Languages: Anything would be OK, Python preferred Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 30-50 hours This is a course on information theory taught by Sir David MacKay at the University of Cambridge. The professor is a very famous scholar in information theory and neural networks, and the textbook for the course is a classic work in the field of information theory. Unfortunately, those whom God loves die young ...","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/#course-resources","text":"Course Website: http://www.inference.org.uk/mackay/itila/ Recordings: https://www.youtube.com/playlist?list=PLruBu5BI5n4aFpG32iMbdWoRVAA-Vcso6 Textbooks: Information Theory, Inference, and Learning Algorithms Assignments: At the end of each lesson video, there are post-lesson exercises from the textbook","title":"Course Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/#rip-prof-david-mackay","text":"","title":"R.I.P Prof. David MacKay"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/","text":"Stanford EE364A: Convex Optimization \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1aPython\uff0c\u5fae\u79ef\u5206\uff0c\u7ebf\u6027\u4ee3\u6570\uff0c\u6982\u7387\u8bba\uff0c\u6570\u503c\u5206\u6790 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 Stephen Boyd \u6559\u6388\u662f\u51f8\u4f18\u5316\u9886\u57df\u7684\u5927\u725b\uff0c\u5176\u7f16\u5199\u7684 Convex Optimization \u8fd9\u672c\u6559\u6750\u88ab\u4f17\u591a\u540d\u6821\u91c7\u7528\u3002\u53e6\u5916\u5176\u7814\u7a76\u56e2\u961f\u8fd8\u4e13\u95e8\u5f00\u53d1\u4e86\u4e00\u4e2a\u7528\u4e8e\u6c42\u89e3\u5e38\u89c1\u51f8\u4f18\u5316\u95ee\u9898\u7684\u7f16\u7a0b\u6846\u67b6\uff0c\u652f\u6301 Python, Julia \u7b49\u4e3b\u6d41\u7f16\u7a0b\u8bed\u8a00\uff0c\u5176\u8bfe\u7a0b\u4f5c\u4e1a\u4e5f\u662f\u91c7\u7528\u8fd9\u4e2a\u7f16\u7a0b\u6846\u67b6\u53bb\u89e3\u51b3\u5b9e\u9645\u751f\u6d3b\u5f53\u4e2d\u7684\u51f8\u4f18\u5316\u95ee\u9898\u3002 \u5728\u5b9e\u9645\u8fd0\u7528\u5f53\u4e2d\uff0c\u4f60\u4f1a\u6df1\u523b\u4f53\u4f1a\u5230\u5bf9\u4e8e\u540c\u4e00\u4e2a\u95ee\u9898\uff0c\u5efa\u6a21\u8fc7\u7a0b\u4e2d\u4e00\u4e2a\u7ec6\u5c0f\u7684\u6539\u53d8\uff0c\u5176\u65b9\u7a0b\u7684\u6c42\u89e3\u96be\u5ea6\u4f1a\u6709\u5929\u58e4\u4e4b\u522b\uff0c\u5982\u4f55\u8ba9\u4f60\u5efa\u6a21\u7684\u65b9\u7a0b\u662f\u201c\u51f8\u201d\u7684\uff0c\u662f\u4e00\u95e8\u827a\u672f\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://stanford.edu/class/ee364a/index.html \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1aD4y1Q7aW \u8bfe\u7a0b\u6559\u6750\uff1a Convex Optimization \u8bfe\u7a0b\u4f5c\u4e1a\uff1a9 \u4e2a Python \u7f16\u7a0b\u4f5c\u4e1a \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/Standford_CVX101 - GitHub \u4e2d\u3002","title":"Standford EE364A: Convex Optimization"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/#stanford-ee364a-convex-optimization","text":"","title":"Stanford EE364A: Convex Optimization"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1aPython\uff0c\u5fae\u79ef\u5206\uff0c\u7ebf\u6027\u4ee3\u6570\uff0c\u6982\u7387\u8bba\uff0c\u6570\u503c\u5206\u6790 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 Stephen Boyd \u6559\u6388\u662f\u51f8\u4f18\u5316\u9886\u57df\u7684\u5927\u725b\uff0c\u5176\u7f16\u5199\u7684 Convex Optimization \u8fd9\u672c\u6559\u6750\u88ab\u4f17\u591a\u540d\u6821\u91c7\u7528\u3002\u53e6\u5916\u5176\u7814\u7a76\u56e2\u961f\u8fd8\u4e13\u95e8\u5f00\u53d1\u4e86\u4e00\u4e2a\u7528\u4e8e\u6c42\u89e3\u5e38\u89c1\u51f8\u4f18\u5316\u95ee\u9898\u7684\u7f16\u7a0b\u6846\u67b6\uff0c\u652f\u6301 Python, Julia \u7b49\u4e3b\u6d41\u7f16\u7a0b\u8bed\u8a00\uff0c\u5176\u8bfe\u7a0b\u4f5c\u4e1a\u4e5f\u662f\u91c7\u7528\u8fd9\u4e2a\u7f16\u7a0b\u6846\u67b6\u53bb\u89e3\u51b3\u5b9e\u9645\u751f\u6d3b\u5f53\u4e2d\u7684\u51f8\u4f18\u5316\u95ee\u9898\u3002 \u5728\u5b9e\u9645\u8fd0\u7528\u5f53\u4e2d\uff0c\u4f60\u4f1a\u6df1\u523b\u4f53\u4f1a\u5230\u5bf9\u4e8e\u540c\u4e00\u4e2a\u95ee\u9898\uff0c\u5efa\u6a21\u8fc7\u7a0b\u4e2d\u4e00\u4e2a\u7ec6\u5c0f\u7684\u6539\u53d8\uff0c\u5176\u65b9\u7a0b\u7684\u6c42\u89e3\u96be\u5ea6\u4f1a\u6709\u5929\u58e4\u4e4b\u522b\uff0c\u5982\u4f55\u8ba9\u4f60\u5efa\u6a21\u7684\u65b9\u7a0b\u662f\u201c\u51f8\u201d\u7684\uff0c\u662f\u4e00\u95e8\u827a\u672f\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://stanford.edu/class/ee364a/index.html \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1aD4y1Q7aW \u8bfe\u7a0b\u6559\u6750\uff1a Convex Optimization \u8bfe\u7a0b\u4f5c\u4e1a\uff1a9 \u4e2a Python \u7f16\u7a0b\u4f5c\u4e1a","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/Standford_CVX101 - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/numerical/","text":"MIT18.330 : Introduction to numerical analysis Descriptions Offered by: MIT Prerequisites:Calculus, Linear Algebra, Probability theory Programming Languages: Julia Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 150 hours While the computational power of computers has been helping people to push boundaries of science, there is a natural barrier between the discrete nature of computers and this continuous world, and how to use discrete representations to estimate and approximate those mathematically continuous concepts is an important theme in numerical analysis. This course will explore various numerical analysis methods in the areas of floating-point representation, equation solving, linear algebra, calculus, and differential equations, allowing you to understand (1) how to design estimation (2) how to estimate errors (3) how to implement algorithms in Julia. There are also plenty of programming assignments to practice these ideas. The designers of this course have also written an open source textbook for this course (see the link below) with plenty of Julia examples. Course Resources Course Website: https://github.com/mitmath/18330 Textbook: https://fncbook.github.io/fnc/frontmatter.html Assignments: 10 problem sets Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPic/MIT18.330 - GitHub","title":"MIT18.330: Introduction to numerical analysis"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/numerical/#mit18330-introduction-to-numerical-analysis","text":"","title":"MIT18.330 : Introduction to numerical analysis"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/numerical/#descriptions","text":"Offered by: MIT Prerequisites:Calculus, Linear Algebra, Probability theory Programming Languages: Julia Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 150 hours While the computational power of computers has been helping people to push boundaries of science, there is a natural barrier between the discrete nature of computers and this continuous world, and how to use discrete representations to estimate and approximate those mathematically continuous concepts is an important theme in numerical analysis. This course will explore various numerical analysis methods in the areas of floating-point representation, equation solving, linear algebra, calculus, and differential equations, allowing you to understand (1) how to design estimation (2) how to estimate errors (3) how to implement algorithms in Julia. There are also plenty of programming assignments to practice these ideas. The designers of this course have also written an open source textbook for this course (see the link below) with plenty of Julia examples.","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/numerical/#course-resources","text":"Course Website: https://github.com/mitmath/18330 Textbook: https://fncbook.github.io/fnc/frontmatter.html Assignments: 10 problem sets","title":"Course Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/numerical/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPic/MIT18.330 - GitHub","title":"Personal Resources"},{"location":"en/%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/15445/","text":"CMU 15-445: Database Systems \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aCMU \u5148\u4fee\u8981\u6c42\uff1aC++\uff0c\u6570\u636e\u7ed3\u6784\u4e0e\u7b97\u6cd5 \u7f16\u7a0b\u8bed\u8a00\uff1aC++ 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\u4e2d\uff0c\u7b2c\u4e8c\u4e2a Project \u662f\u505a B \u6811\uff0c\u7b2c\u56db\u4e2a Project \u662f\u505a\u5e76\u53d1\u63a7\u5236\u3002 \u5728 Fall2021 \u4e2d\uff0c\u7b2c\u4e8c\u4e2a Project \u662f\u505a\u7f13\u5b58\u6c60\u7ba1\u7406\uff0c\u7b2c\u4e09\u4e2a Project \u662f\u505a\u54c8\u5e0c\u7d22\u5f15\uff0c\u7b2c\u56db\u4e2a Project \u662f\u505a\u5e76\u53d1\u63a7\u5236\u3002 \u5982\u679c\u5927\u5bb6\u6709\u7cbe\u529b\u7684\u8bdd\u53ef\u4ee5\u90fd\u53bb\u5c1d\u8bd5\u4e00\u4e0b\uff0c\u6216\u8005\u5728\u5bf9\u4e66\u4e2d\u5185\u5bb9\u7406\u89e3\u4e0d\u662f\u5f88\u900f\u5f7b\u7684\u65f6\u5019\uff0c\u5c1d\u8bd5\u7528\u4ee3\u7801\u5199\u4e00\u4e2a\u4f1a\u52a0\u6df1\u4f60\u7684\u7406\u89e3\u3002 \u8d44\u6e90\u6c47\u603b @ysj1173886760 \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 ysj1173886760/Learning: db - GitHub \u4e2d\u3002 \u7531\u4e8e Andy \u7684\u8981\u6c42\uff0c\u4ed3\u5e93\u4e2d\u6ca1\u6709 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\u8981\u8bfb\uff0c\u63a8\u8350\u7ed9\u5e0c\u671b\u8fdb\u9636\u6570\u636e\u5e93\u7684\u5c0f\u4f19\u4f34\u3002@ysj1173886760 \u76ee\u524d\u4e5f\u5728\u8ddf\u8fdb\u8fd9\u95e8\u8bfe\uff0c\u5b8c\u6210\u540e\u4f1a\u5728\u8fd9\u91cc\u63d0 PR \u4ee5\u63d0\u4f9b\u8fdb\u9636\u7684\u6307\u5bfc\u3002","title":"CMU 15-445: Database Systems"},{"location":"en/%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/15445/#cmu-15-445-database-systems","text":"","title":"CMU 15-445: Database Systems"},{"location":"en/%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/15445/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aCMU \u5148\u4fee\u8981\u6c42\uff1aC++\uff0c\u6570\u636e\u7ed3\u6784\u4e0e\u7b97\u6cd5 \u7f16\u7a0b\u8bed\u8a00\uff1aC++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u4f5c\u4e3a CMU \u6570\u636e\u5e93\u7684\u5165\u95e8\u8bfe\uff0c\u8fd9\u95e8\u8bfe\u7531\u6570\u636e\u5e93\u9886\u57df\u7684\u5927\u725b Andy Pavlo \u8bb2\u6388\uff08\u201c\u8fd9\u4e2a\u4e16\u754c\u4e0a\u6211\u53ea\u5728\u4e4e\u4e24\u4ef6\u4e8b\uff0c\u4e00\u662f\u6211\u7684\u8001\u5a46\uff0c\u4e8c\u5c31\u662f\u6570\u636e\u5e93\u201d\uff09\u300215-445 \u4f1a\u81ea\u5e95\u5411\u4e0a\u5730\u6559\u4f60\u6570\u636e\u5e93\u7cfb\u7edf\u7684\u57fa\u672c\u7ec4\u6210\u90e8\u5206\uff1a\u5b58\u50a8\u3001\u7d22\u5f15\u3001\u67e5\u8be2\uff0c\u4ee5\u53ca\u5e76\u53d1\u4e8b\u52a1\u63a7\u5236\u3002 \u8fd9\u95e8\u8bfe\u7684\u4eae\u70b9\u5728\u4e8e CMU db \u4e13\u95e8\u4e3a\u6b64\u8bfe\u5f00\u53d1\u4e86\u4e00\u4e2a\u6559\u5b66\u7528\u7684\u5173\u7cfb\u578b\u6570\u636e\u5e93 bustub \uff0c\u5e76\u8981\u6c42\u4f60\u5bf9\u8fd9\u4e2a\u6570\u636e\u5e93\u7684\u7ec4\u6210\u90e8\u5206\u8fdb\u884c\u4fee\u6539\uff0c\u5b9e\u73b0\u4e0a\u8ff0\u90e8\u4ef6\u7684\u529f\u80fd\u3002\u6b64\u5916 bustub \u4f5c\u4e3a\u4e00\u4e2a C++ \u7f16\u5199\u7684\u4e2d\u5c0f\u578b\u9879\u76ee\u6db5\u76d6\u4e86\u7a0b\u5e8f\u6784\u5efa\u3001\u4ee3\u7801\u89c4\u8303\u3001\u5355\u5143\u6d4b\u8bd5\u7b49\u4f17\u591a\u8981\u6c42\uff0c\u53ef\u4ee5\u4f5c\u4e3a\u4e00\u4e2a\u4f18\u79c0\u7684\u5f00\u6e90\u9879\u76ee\u5b66\u4e60\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/15445/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a Fall2019 , Fall2020 , Fall2021 \u8bfe\u7a0b\u89c6\u9891\uff1a\u8bfe\u7a0b\u7f51\u7ad9\u514d\u8d39\u89c2\u770b \u8bfe\u7a0b\u6559\u6750\uff1aDatabase System Concepts \u8bfe\u7a0b\u4f5c\u4e1a\uff1a4 \u4e2a Project \u5728 Fall2019 \u4e2d\uff0c\u7b2c\u4e8c\u4e2a Project \u662f\u505a\u54c8\u5e0c\u7d22\u5f15\uff0c\u7b2c\u56db\u4e2a Project \u662f\u505a\u65e5\u5fd7\u4e0e\u6062\u590d\u3002 \u5728 Fall2020 \u4e2d\uff0c\u7b2c\u4e8c\u4e2a Project \u662f\u505a B \u6811\uff0c\u7b2c\u56db\u4e2a Project \u662f\u505a\u5e76\u53d1\u63a7\u5236\u3002 \u5728 Fall2021 \u4e2d\uff0c\u7b2c\u4e8c\u4e2a Project \u662f\u505a\u7f13\u5b58\u6c60\u7ba1\u7406\uff0c\u7b2c\u4e09\u4e2a Project \u662f\u505a\u54c8\u5e0c\u7d22\u5f15\uff0c\u7b2c\u56db\u4e2a Project \u662f\u505a\u5e76\u53d1\u63a7\u5236\u3002 \u5982\u679c\u5927\u5bb6\u6709\u7cbe\u529b\u7684\u8bdd\u53ef\u4ee5\u90fd\u53bb\u5c1d\u8bd5\u4e00\u4e0b\uff0c\u6216\u8005\u5728\u5bf9\u4e66\u4e2d\u5185\u5bb9\u7406\u89e3\u4e0d\u662f\u5f88\u900f\u5f7b\u7684\u65f6\u5019\uff0c\u5c1d\u8bd5\u7528\u4ee3\u7801\u5199\u4e00\u4e2a\u4f1a\u52a0\u6df1\u4f60\u7684\u7406\u89e3\u3002","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/15445/#_3","text":"@ysj1173886760 \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 ysj1173886760/Learning: db - GitHub \u4e2d\u3002 \u7531\u4e8e Andy \u7684\u8981\u6c42\uff0c\u4ed3\u5e93\u4e2d\u6ca1\u6709 Project \u7684\u5b9e\u73b0\uff0c\u53ea\u6709 Homework \u7684 Solution\u3002\u7279\u522b\u7684\uff0c\u5bf9\u4e8e Homework1\uff0c@ysj1173886760 \u8fd8\u5199\u4e86\u4e00\u4e2a Shell \u811a\u672c\u6765\u5e2e\u5927\u5bb6\u6267\u884c\u81ea\u52a8\u5224\u5206\u3002 \u53e6\u5916\u5728\u8bfe\u7a0b\u7ed3\u675f\u540e\uff0c\u63a8\u8350\u9605\u8bfb\u4e00\u7bc7\u8bba\u6587 Architecture Of a Database System \uff0c\u5bf9\u5e94\u7684\u4e2d\u6587\u7248\u4e5f\u5728\u4e0a\u8ff0\u4ed3\u5e93\u4e2d\u3002\u8bba\u6587\u91cc\u7efc\u8ff0\u4e86\u6570\u636e\u5e93\u7cfb\u7edf\u7684\u6574\u4f53\u67b6\u6784\uff0c\u8ba9\u5927\u5bb6\u53ef\u4ee5\u5bf9\u6570\u636e\u5e93\u6709\u4e00\u4e2a\u66f4\u52a0\u5168\u9762\u7684\u89c6\u91ce\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/15445/#_4","text":"CMU15-721 \u4e3b\u8981\u8bb2\u4e3b\u5b58\u6570\u636e\u5e93\u6709\u5173\u7684\u5185\u5bb9\uff0c\u6bcf\u8282\u8bfe\u90fd\u6709\u5bf9\u5e94\u7684 paper \u8981\u8bfb\uff0c\u63a8\u8350\u7ed9\u5e0c\u671b\u8fdb\u9636\u6570\u636e\u5e93\u7684\u5c0f\u4f19\u4f34\u3002@ysj1173886760 \u76ee\u524d\u4e5f\u5728\u8ddf\u8fdb\u8fd9\u95e8\u8bfe\uff0c\u5b8c\u6210\u540e\u4f1a\u5728\u8fd9\u91cc\u63d0 PR \u4ee5\u63d0\u4f9b\u8fdb\u9636\u7684\u6307\u5bfc\u3002","title":"\u540e\u7eed\u8bfe\u7a0b"},{"location":"en/%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS122/","text":"Caltech CS 122: Database System Implementation \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aCaltech \u5148\u4fee\u8981\u6c42\uff1a\u65e0 \u7f16\u7a0b\u8bed\u8a00\uff1aJava \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 \u52a0\u5dde\u7406\u5de5\u7684\u8fd9\u95e8\u8bfe\uff0c\u4e0d\u540c\u4e8e\u6ca1\u6709\u63d0\u4f9b SQL \u5c42\u529f\u80fd\u7684 CMU15-445 \u8bfe\u7a0b\u3002CS122 \u8bfe\u7a0b Lab \u7684\u4fa7\u91cd\u70b9\u5728\u4e8e SQL \u5c42\u7684\u76f8\u5173\u5b9e\u73b0\uff0c\u6d89\u53ca\u67e5\u8be2\u4f18\u5316\u5668\u7684\u5404\u4e2a\u6a21\u5757\uff0c\u6bd4\u5982SQL\u7684\u89e3\u6790\uff0cTranslate\uff0c\u5982\u4f55\u5b9e\u73b0 Join\uff0c\u7edf\u8ba1\u4fe1\u606f\u4ee5\u53ca\u4ee3\u4ef7\u4f30\u8ba1\uff0c\u5b50\u67e5\u8be2\u5b9e\u73b0\uff0cAgg\uff0cGroup By \u7684\u5b9e\u73b0\u7b49\u3002\u9664\u6b64\u4e4b\u5916\uff0c\u8fd8\u6709 B+\u6811\uff0cWAL \u76f8\u5173\u5b9e\u9a8c\u3002\u672c\u95e8\u8bfe\u7a0b\u9002\u5408\u5728\u5b66\u5b8c CMU15-445 \u8bfe\u7a0b\u4e4b\u540e\uff0c\u5bf9\u67e5\u8be2\u4f18\u5316\u76f8\u5173\u5185\u5bb9\u6709\u5174\u8da3\u7684\u540c\u5b66\u3002 \u4e0b\u9762\u4ecb\u7ecd\u4e00\u4e0b\u8fd9\u95e8\u8bfe\u7684\u524d 3 \u4e2a Assignment \u4e5f\u5c31\u662f\u5b9e\u9a8c Lab \u6240\u8981\u5b9e\u73b0\u7684\u529f\u80fd\uff1a Assignment1 \u4e3a NanoDB \u63d0\u4f9b delete\uff0cupdate \u8bed\u53e5\u7684\u652f\u6301\u3002 \u4e3a Buffer Pool Manager \u6dfb\u52a0\u5408\u9002\u7684 pin/unpin \u4ee3\u7801\u3002 \u63d0\u5347 insert \u8bed\u53e5\u7684\u6027\u80fd\uff0c \u540c\u65f6\u4e0d\u4f7f\u6570\u636e\u5e93\u6587\u4ef6\u5927\u5c0f\u8fc7\u5206\u81a8\u80c0\u3002 Assignment2 \u5b9e\u73b0\u4e00\u4e2a\u7b80\u5355\u7684\u8ba1\u5212\u751f\u6210\u5668\uff0c\u5c06\u5404\u79cd\u5df2\u7ecf Parser \u8fc7\u7684 SQL \u8bed\u53e5\u8f6c\u5316\u4e3a\u53ef\u6267\u884c\u7684\u6267\u884c\u8ba1\u5212\u3002 \u4f7f\u7528 nested-loop join \u7b97\u6cd5\uff0c\u5b9e\u73b0\u652f\u6301 inner- and outer-join \u7684 Join \u8ba1\u5212\u8282\u70b9\u3002 \u6dfb\u52a0\u4e00\u4e9b\u5355\u5143\u6d4b\u8bd5\uff0c \u4fdd\u8bc1 inner- and outer-join \u529f\u80fd\u5b9e\u73b0\u6b63\u786e\u3002 Assignment3 \u5b8c\u6210\u6536\u96c6\u8868\u7684\u7edf\u8ba1\u4fe1\u606f\u3002 \u5b8c\u6210\u5404\u79cd\u8ba1\u5212\u8282\u70b9\u7684\u8ba1\u5212\u6210\u672c\u8ba1\u7b97\u3002 \u8ba1\u7b97\u53ef\u51fa\u73b0\u5728\u6267\u884c\u8ba1\u5212\u4e2d\u7684\u5404\u79cd\u8c13\u8bcd\u7684\u9009\u62e9\u6027\u3002 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\u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS186 - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6/Data100/","text":"UCB Data100: Principles and Techniques of Data Science Description Offered by: UC Berkeley Prerequisites: CS61A\uff0cLinear Algebra Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 80 hours This is Berkeley's introductory course in data science, covering the basics of data cleaning, feature extraction, data visualization, machine learning and inference, as well as common data science tools such as Pandas, Numpy, and Matplotlib. The course is also rich in interesting programming assignments, which is one of the highlights of the course. Resources Course Website: https://ds100.org/fa21/ Records: refer to course website Textbook: https://www.textbook.ds100.org/intro.html Assignments: refer to course website","title":"UCB Data100: Principles and Techniques of Data Science"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6/Data100/#ucb-data100-principles-and-techniques-of-data-science","text":"","title":"UCB Data100: Principles and Techniques of Data Science"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6/Data100/#description","text":"Offered by: UC Berkeley Prerequisites: CS61A\uff0cLinear Algebra Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 80 hours This is Berkeley's introductory course in data science, covering the basics of data cleaning, feature extraction, data visualization, machine learning and inference, as well as common data science tools such as Pandas, Numpy, and Matplotlib. The course is also rich in interesting programming assignments, which is one of the highlights of the course.","title":"Description"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6/Data100/#resources","text":"Course Website: https://ds100.org/fa21/ Records: refer to course website Textbook: https://www.textbook.ds100.org/intro.html Assignments: refer to course website","title":"Resources"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/","text":"Coursera: Algorithms I & II Descriptions Offered by: Princeton Prerequisites: CS61A Programming Languages: Java Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 60 hours This is the highest rated algorithms course on Coursera , and Robert Sedgewick has the magic to make even the most complex algorithms incredibly easy to understand. To be honest, the KMP and network flow algorithms that I have been struggling with for years were made clear to me in this course, and I can even write derivations and proofs for both of them two years later. Do you feel that you forget the algorithms quickly after learning them? I think the key to fully grasping an algorithm lies in understanding the three points as follows: Why should do this? (Correctness derivation, or the essence of the entire algorithm.) How to implement it? (Talk is cheap. Show me the code.) How to use it to solve practical problems? (Bridge the gap between theory and real life.) The composition of this course covers the three points above very well. Watching the course videos and reading the professor's textbook will help you understand the essence of the algorithm and allow you to tell others why the algorithm should look like this in very simple and vivid terms. After understanding the algorithms, you can read the professor's code implementation of all the data structures and algorithms taught in the course. Note that these codes are not demos, but production-ready, time-efficient implementations. They have extensive annotations and comments, and the modularization is also quite good. I learned a lot by just reading the codes. Finally, the most exciting part of the course is the 10 high-quality projects, all with real-world backgrounds, rich test cases, and an automated scoring system (code style is also a part of the scoring). You'll get a taste of algorithms in real life. Course Resources Course Website: Algorithm I , Algorithm II Recordings: Coursera: Algorithm I , Coursera: lgorithm II , CUvids: Algorithms, 4th Edition Textbooks: Algorithms, 4th Edition Assignments: 10 Projects, the course website has specific requirements Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/Princeton-Algorithm - GitHub .","title":"Coursera: Algorithms I & II"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/#coursera-algorithms-i-ii","text":"","title":"Coursera: Algorithms I & II"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/#descriptions","text":"Offered by: Princeton Prerequisites: CS61A Programming Languages: Java Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 60 hours This is the highest rated algorithms course on Coursera , and Robert Sedgewick has the magic to make even the most complex algorithms incredibly easy to understand. To be honest, the KMP and network flow algorithms that I have been struggling with for years were made clear to me in this course, and I can even write derivations and proofs for both of them two years later. Do you feel that you forget the algorithms quickly after learning them? I think the key to fully grasping an algorithm lies in understanding the three points as follows: Why should do this? (Correctness derivation, or the essence of the entire algorithm.) How to implement it? (Talk is cheap. Show me the code.) How to use it to solve practical problems? (Bridge the gap between theory and real life.) The composition of this course covers the three points above very well. Watching the course videos and reading the professor's textbook will help you understand the essence of the algorithm and allow you to tell others why the algorithm should look like this in very simple and vivid terms. After understanding the algorithms, you can read the professor's code implementation of all the data structures and algorithms taught in the course. Note that these codes are not demos, but production-ready, time-efficient implementations. They have extensive annotations and comments, and the modularization is also quite good. I learned a lot by just reading the codes. Finally, the most exciting part of the course is the 10 high-quality projects, all with real-world backgrounds, rich test cases, and an automated scoring system (code style is also a part of the scoring). You'll get a taste of algorithms in real life.","title":"Descriptions"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/#course-resources","text":"Course Website: Algorithm I , Algorithm II Recordings: Coursera: Algorithm I , Coursera: lgorithm II , CUvids: Algorithms, 4th Edition Textbooks: Algorithms, 4th Edition Assignments: 10 Projects, the course website has specific requirements","title":"Course Resources"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/Princeton-Algorithm - GitHub .","title":"Personal Resources"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS170/","text":"CS170: Efficient Algorithms and Intractable Problems \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61B, CS70 \u7f16\u7a0b\u8bed\u8a00\uff1aLaTeX \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a60 \u5c0f\u65f6 \u4f2f\u514b\u5229\u7684\u7b97\u6cd5\u8bbe\u8ba1\u8bfe\uff0c\u66f4\u6ce8\u91cd\u7b97\u6cd5\u7684\u7406\u8bba\u57fa\u7840\u4e0e\u590d\u6742\u5ea6\u5206\u6790\u3002\u8bfe\u7a0b\u5185\u5bb9\u6db5\u76d6\u4e86\u5206\u6cbb\u3001\u56fe\u7b97\u6cd5\u3001\u6700\u77ed\u8def\u3001\u751f\u6210\u6811\u3001\u8d2a\u5fc3\u3001\u52a8\u89c4\u3001\u5e76\u67e5\u96c6\u3001\u7ebf\u6027\u89c4\u5212\u3001\u7f51\u7edc\u6d41\u3001NP \u95ee\u9898\u3001\u968f\u673a\u7b97\u6cd5\u3001\u54c8\u5e0c\u7b97\u6cd5\u7b49\u7b49\u3002 \u8fd9\u95e8\u8bfe\u7684\u6559\u6750\u5199\u7684\u5f88\u597d\uff0c\u8bc1\u660e\u6d45\u663e\u6613\u61c2\uff0c\u975e\u5e38\u9002\u5408\u4f5c\u4e3a\u5de5\u5177\u4e66\u67e5\u9605\u3002\u53e6\u5916\uff0c\u8fd9\u95e8\u8bfe\u53ea\u6709\u4e66\u9762\u4f5c\u4e1a\uff0c\u5e76\u4e14\u63a8\u8350\u7528 LaTeX \u7f16\u5199\uff0c\u5927\u5bb6\u53ef\u4ee5\u501f\u6b64\u673a\u4f1a\u953b\u70bc\u81ea\u5df1\u7684 LaTeX \u6280\u5de7\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://cs170.org/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1BU4y1b7RK \u8bfe\u7a0b\u6559\u6750\uff1a\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9 notes \u8bfe\u7a0b\u4f5c\u4e1a\uff1a13 \u6b21\u4e66\u9762\u4f5c\u4e1a\uff0c\u7528 LaTeX \u7f16\u5199 \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/UCB-CS170 - 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GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS61B/","text":"CS61B: Data Structures and Algorithms Descriptions Offered by: UC Berkeley Prerequisites: CS61A Programming Languages: Java Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 60 hours It is the second course of UC Berkeley's CS61 series. It mainly focuses on the design of data structures and algorithms as well as giving students the opportunity to be exposed to thousands of lines of engineering code and gain a preliminary understanding of software engineering through Java. I took the version for 2018 Spring. Josh Hug, the instructor, generously made the autograder open-source. You can use gradescope invitation code published on the website for free and easily test your implementation. All programming assignments in this course are done in Java. Students without Java experience don't have to worry. There will be detailed tutorials in the course from the configuration of IDEA to the core syntax and features of Java. The quality of homework in this class is also unparalleled. The 14 labs will allow you to implement most of the data structures mentioned in the class by yourself, and the 10 homework will allow you to use data structures and algorithms to solve practical problems. In addition, there are 3 projects that give you the opportunity to be exposed to thousands of lines of engineering code and enhance your Java skills in practice. Resources Course Resources Course Website: https://sp18.datastructur.es/ Recordings: refer to the course website Textbook: None Assignments: Slightly different every year. In the spring semester of 2018, there are 14 Labs, 10 Homeworks and 3 Projects. Please refer to the course website for specific requirements. Personal resources All resources and homework implementations used by @PKUFlyingPig in this course are summarized in PKUFlyingPig/CS61B - GitHub .","title":"UCB CS61B: Data Structures and Algorithms"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS61B/#cs61b-data-structures-and-algorithms","text":"","title":"CS61B: Data Structures and Algorithms"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS61B/#descriptions","text":"Offered by: UC Berkeley Prerequisites: CS61A Programming Languages: Java Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 60 hours It is the second course of UC Berkeley's CS61 series. It mainly focuses on the design of data structures and algorithms as well as giving students the opportunity to be exposed to thousands of lines of engineering code and gain a preliminary understanding of software engineering through Java. I took the version for 2018 Spring. Josh Hug, the instructor, generously made the autograder open-source. You can use gradescope invitation code published on the website for free and easily test your implementation. All programming assignments in this course are done in Java. Students without Java experience don't have to worry. There will be detailed tutorials in the course from the configuration of IDEA to the core syntax and features of Java. The quality of homework in this class is also unparalleled. The 14 labs will allow you to implement most of the data structures mentioned in the class by yourself, and the 10 homework will allow you to use data structures and algorithms to solve practical problems. In addition, there are 3 projects that give you the opportunity to be exposed to thousands of lines of engineering code and enhance your Java skills in practice.","title":"Descriptions"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS61B/#resources","text":"","title":"Resources"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS61B/#course-resources","text":"Course Website: https://sp18.datastructur.es/ Recordings: refer to the course website Textbook: None Assignments: Slightly different every year. In the spring semester of 2018, there are 14 Labs, 10 Homeworks and 3 Projects. Please refer to the course website for specific requirements.","title":"Course Resources"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS61B/#personal-resources","text":"All resources and homework implementations used by @PKUFlyingPig in this course are summarized in PKUFlyingPig/CS61B - GitHub .","title":"Personal resources"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/CS189/","text":"CS189: Introduction to Machine Learning \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS188, CS70 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u6211\u6ca1\u6709\u7cfb\u7edf\u4e0a\u8fc7\uff0c\u53ea\u662f\u628a\u5b83\u7684\u8bfe\u7a0b notes \u4f5c\u4e3a\u5de5\u5177\u4e66\u67e5\u9605\u3002\u4e0d\u8fc7\u4ece\u8bfe\u7a0b\u7f51\u7ad9\u4e0a\u6765\u770b\uff0c\u5b83\u6bd4 CS229 \u597d\u7684\u662f\u5f00\u6e90\u4e86\u6240\u6709 homework \u7684\u4ee3\u7801\u4ee5\u53ca gradescope \u7684 autograder\u3002\u540c\u6837\uff0c\u8fd9\u95e8\u8bfe\u8bb2\u5f97\u76f8\u5f53\u7406\u8bba\u4e14\u6df1\u5165\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.eecs189.org/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/playlist?list=PLOOm2AoWIPEyZazQVnIcaK2KnezpGZV-X \u8bfe\u7a0b\u6559\u6750\uff1a https://www.eecs189.org/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://www.eecs189.org/","title":"UCB CS189: Introduction to Machine Learning"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/CS189/#cs189-introduction-to-machine-learning","text":"","title":"CS189: Introduction to Machine Learning"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/CS189/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS188, CS70 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u6211\u6ca1\u6709\u7cfb\u7edf\u4e0a\u8fc7\uff0c\u53ea\u662f\u628a\u5b83\u7684\u8bfe\u7a0b notes \u4f5c\u4e3a\u5de5\u5177\u4e66\u67e5\u9605\u3002\u4e0d\u8fc7\u4ece\u8bfe\u7a0b\u7f51\u7ad9\u4e0a\u6765\u770b\uff0c\u5b83\u6bd4 CS229 \u597d\u7684\u662f\u5f00\u6e90\u4e86\u6240\u6709 homework \u7684\u4ee3\u7801\u4ee5\u53ca gradescope \u7684 autograder\u3002\u540c\u6837\uff0c\u8fd9\u95e8\u8bfe\u8bb2\u5f97\u76f8\u5f53\u7406\u8bba\u4e14\u6df1\u5165\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/CS189/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.eecs189.org/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/playlist?list=PLOOm2AoWIPEyZazQVnIcaK2KnezpGZV-X \u8bfe\u7a0b\u6559\u6750\uff1a https://www.eecs189.org/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://www.eecs189.org/","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/CS229/","text":"CS229: Machine Learning \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u9ad8\u6570\uff0c\u6982\u7387\u8bba\uff0cPython\uff0c\u9700\u8981\u8f83\u6df1\u539a\u7684\u6570\u5b66\u529f\u5e95 \u7f16\u7a0b\u8bed\u8a00\uff1a\u65e0 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u540c\u6837\u662f\u5434\u6069\u8fbe\u8bb2\u6388\uff0c\u4f46\u662f\u8fd9\u662f\u4e00\u95e8\u7814\u7a76\u751f\u8bfe\u7a0b\uff0c\u6240\u4ee5\u66f4\u504f\u91cd\u6570\u5b66\u7406\u8bba\uff0c\u4e0d\u6ee1\u8db3\u4e8e\u8c03\u5305\u800c\u60f3\u6df1\u5165\u7406\u89e3\u7b97\u6cd5\u672c\u8d28\uff0c\u6216\u8005\u6709\u5fd7\u4e8e\u4ece\u4e8b\u673a\u5668\u5b66\u4e60\u7406\u8bba\u7814\u7a76\u7684\u540c\u5b66\u53ef\u4ee5\u5b66\u4e60\u8fd9\u95e8\u8bfe\u7a0b\u3002\u8bfe\u7a0b\u7f51\u7ad9\u4e0a\u63d0\u4f9b\u4e86\u6240\u6709\u7684\u8bfe\u7a0b notes\uff0c\u5199\u5f97\u975e\u5e38\u4e13\u4e1a\u4e14\u7406\u8bba\uff0c\u9700\u8981\u4e00\u5b9a\u7684\u6570\u5b66\u529f\u5e95\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://cs229.stanford.edu/syllabus.html \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1JE411w7Ub \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0\uff0c\u8bfe\u7a0b notes \u5199\u5f97\u975e\u5e38\u597d \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u4e0d\u5bf9\u516c\u4f17\u5f00\u653e \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS229 - 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He is one of the founders of the famous online education platform Coursera , and also a famous professor at Stanford. This introductory machine learning course must be one of his famous works (the other is his deep learning course), and has hundreds of thousands of learners on Coursera (note that these are people who paid for the certificate, which costs several hundred dollars), and the number of nonpaying learners should be far more than that. The class is extremely friendly to novices, and Andrew has the ability to make machine learning as straightforward as 1+1=2. You'll learn about linear regression, logistic regression, support vector machines, unsupervised learning, dimensionality reduction, anomaly detection, and recommender systems, etc. and solidify your understanding with hands-on programming. The quality of the assignments needs no word to say. With detailed code frameworks and practical background, you can use what you've learned to solve real problems. 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Also, course material is available on Coursera.","title":"Coursera: Machine Learning"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/ML/#coursera-machine-learning","text":"","title":"Coursera: Machine Learning"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/ML/#descriptions","text":"Offered by: Stanford Prerequisites: entry level of AI and proficient in Python Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours When it comes to Andrew Ng, no one in the AI community should be unaware of him. He is one of the founders of the famous online education platform Coursera , and also a famous professor at Stanford. 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Also, course material is available on Coursera.","title":"Personal Resources"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%B3%BB%E7%BB%9F/CMU10-414/","text":"CMU 10-414/714: Deep Learning Systems \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aCMU \u5148\u4fee\u8981\u6c42\uff1a\u7cfb\u7edf\u5165\u95e8(eg.15-213)\u3001\u6df1\u5ea6\u5b66\u4e60\u5165\u95e8\u3001\u57fa\u672c\u7684\u6570\u5b66\u77e5\u8bc6 \u7f16\u7a0b\u8bed\u8a00\uff1aN/A\uff08\u636e\u8bfe\u7a0b\u4e3b\u9875\uff0c\u8981\u6c42\u719f\u6089Python\u3001C/C++\uff09 \u8bfe\u7a0b\u96be\u5ea6\uff1aN/A \u9884\u8ba1\u5b66\u65f6\uff1aN/A \u8fd9\u662f CMU 2022\u5e74\u79cb\u5b63\u5b66\u671f\u5f00\u8bbe\u7684\u4e00\u95e8\u65b0\u8bfe\uff0c\u805a\u7126\u4e8e\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u7684\u5177\u4f53\u5b9e\u73b0\uff0c\u8bfe\u7a0b Project \u4f1a\u5b9e\u73b0\u4e00\u4e2a\u8ff7\u4f60\u7684\u7c7b\u4f3c\u4e8e Pytorch 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\u8fd9\u95e8\u8bfe\u662f\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u9886\u57df\u7684\u9876\u5c16\u5b66\u8005\u9648\u5929\u5947\u57282022\u5e74\u6691\u671f\u5f00\u8bbe\u7684\u4e00\u95e8\u5728\u7ebf\u8bfe\u7a0b\u3002\u5176\u5b9e\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u65e0\u8bba\u5728\u5de5\u4e1a\u754c\u8fd8\u662f\u5b66\u672f\u754c\u4ecd\u7136\u662f\u4e00\u4e2a\u975e\u5e38\u524d\u6cbf\u4e14\u5feb\u901f\u66f4\u8fed\u7684\u9886\u57df\uff0c\u56fd\u5185\u5916\u6b64\u524d\u8fd8\u6ca1\u6709\u4e3a\u8fd9\u4e2a\u65b9\u5411\u4e13\u95e8\u5f00\u8bbe\u7684\u76f8\u5173\u8bfe\u7a0b\u3002\u56e0\u6b64\u5982\u679c\u5bf9\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u611f\u5174\u8da3\u60f3\u6709\u4e2a\u5168\u8c8c\u6027\u7684\u611f\u77e5\u7684\u8bdd\uff0c\u53ef\u4ee5\u5b66\u4e60\u4e00\u4e0b\u8fd9\u95e8\u8bfe\u3002 \u672c\u8bfe\u7a0b\u4e3b\u8981\u4ee5 Apache TVM 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Compilation"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%B3%BB%E7%BB%9F/MLC/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aBilibili \u5927\u5b66 \u5148\u4fee\u8981\u6c42\uff1a\u673a\u5668\u5b66\u4e60/\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a30\u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u662f\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u9886\u57df\u7684\u9876\u5c16\u5b66\u8005\u9648\u5929\u5947\u57282022\u5e74\u6691\u671f\u5f00\u8bbe\u7684\u4e00\u95e8\u5728\u7ebf\u8bfe\u7a0b\u3002\u5176\u5b9e\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u65e0\u8bba\u5728\u5de5\u4e1a\u754c\u8fd8\u662f\u5b66\u672f\u754c\u4ecd\u7136\u662f\u4e00\u4e2a\u975e\u5e38\u524d\u6cbf\u4e14\u5feb\u901f\u66f4\u8fed\u7684\u9886\u57df\uff0c\u56fd\u5185\u5916\u6b64\u524d\u8fd8\u6ca1\u6709\u4e3a\u8fd9\u4e2a\u65b9\u5411\u4e13\u95e8\u5f00\u8bbe\u7684\u76f8\u5173\u8bfe\u7a0b\u3002\u56e0\u6b64\u5982\u679c\u5bf9\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u611f\u5174\u8da3\u60f3\u6709\u4e2a\u5168\u8c8c\u6027\u7684\u611f\u77e5\u7684\u8bdd\uff0c\u53ef\u4ee5\u5b66\u4e60\u4e00\u4e0b\u8fd9\u95e8\u8bfe\u3002 \u672c\u8bfe\u7a0b\u4e3b\u8981\u4ee5 Apache TVM \u8fd9\u4e00\u4e3b\u6d41\u7684\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u6846\u67b6\u4e3a\u4f8b\uff08\u9648\u5929\u5947\u662f\u8fd9\u4e2a\u6846\u67b6\u7684\u521b\u59cb\u4eba\u4e4b\u4e00\uff09\uff0c\u805a\u7126\u4e8e\u5982\u4f55\u5c06\u5f00\u53d1\u6a21\u5f0f\u4e0b\uff08\u5982 Tensorflow, Pytorch, Jax\uff09\u7684\u5404\u7c7b\u673a\u5668\u5b66\u4e60\u6a21\u578b\uff0c\u901a\u8fc7\u4e00\u5957\u666e\u9002\u7684\u62bd\u8c61\u548c\u4f18\u5316\u7b97\u6cd5\uff0c\u53d8\u6362\u4e3a\u62e5\u6709\u66f4\u9ad8\u6027\u80fd\u5e76\u4e14\u9002\u914d\u5404\u7c7b\u5e95\u5c42\u786c\u4ef6\u7684\u90e8\u7f72\u6a21\u5f0f\u3002\u8bfe\u7a0b\u8bb2\u6388\u7684\u77e5\u8bc6\u70b9\u90fd\u662f\u76f8\u5bf9 High-Level \u7684\u5b8f\u89c2\u6982\u5ff5\uff0c\u540c\u65f6\u6bcf\u8282\u8bfe\u90fd\u4f1a\u6709\u4e00\u4e2a\u914d\u5957\u7684 Jupyter Notebook \u6765\u901a\u8fc7\u5177\u4f53\u7684\u4ee3\u7801\u8bb2\u89e3\u77e5\u8bc6\u70b9\uff0c\u56e0\u6b64\u5982\u679c\u4ece\u4e8b TVM \u76f8\u5173\u7684\u7f16\u7a0b\u5f00\u53d1\u7684\u8bdd\uff0c\u8fd9\u95e8\u8bfe\u6709\u4e30\u5bcc\u4e14\u89c4\u8303\u7684\u4ee3\u7801\u793a\u4f8b\u4ee5\u4f9b\u53c2\u8003\u3002 \u6240\u6709\u7684\u8bfe\u7a0b\u8d44\u6e90\u5168\u90e8\u5f00\u6e90\u5e76\u4e14\u6709\u4e2d\u6587\u548c\u82f1\u6587\u4e24\u4e2a\u7248\u672c\uff0cB\u7ad9\u548c\u6cb9\u7ba1\u5206\u522b\u6709\u4e2d\u6587\u548c\u82f1\u6587\u7684\u8bfe\u7a0b\u5f55\u5f71\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%B3%BB%E7%BB%9F/MLC/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://mlc.ai/summer22-zh/ \u8bfe\u7a0b\u89c6\u9891\uff1a Bilibili \u8bfe\u7a0b\u7b14\u8bb0\uff1a https://mlc.ai/zh/index.html \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://github.com/mlc-ai/notebooks/blob/main/assignment","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CMU10-708/","text":"CMU 10-708: Probabilistic Graphical Models \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aCMU \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://sailinglab.github.io/pgm-spring-2019/ \u8fd9\u4e2a\u7f51\u7ad9\u5305\u542b\u4e86\u6240\u6709\u7684\u8d44\u6e90\uff1aslides, nots, video, homework, project \u8fd9\u95e8\u8bfe\u7a0b\u662f CMU \u7684\u56fe\u6a21\u578b\u57fa\u7840 + \u8fdb\u9636\u8bfe\uff0c\u6388\u8bfe\u8001\u5e08\u4e3a Eric P. Xing\uff0c\u6db5\u76d6\u4e86\u56fe\u6a21\u578b\u57fa\u7840\uff0c\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u5728\u5f3a\u5316\u5b66\u4e60\u4e2d\u7684\u5e94\u7528\uff0c\u4ee5\u53ca\u975e\u53c2\u6570\u65b9\u6cd5\u3002\u76f8\u5f53\u786c\u6838","title":"CMU 10-708: Probabilistic Graphical Models"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CMU10-708/#cmu-10-708-probabilistic-graphical-models","text":"","title":"CMU 10-708: Probabilistic Graphical Models"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CMU10-708/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aCMU \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://sailinglab.github.io/pgm-spring-2019/ \u8fd9\u4e2a\u7f51\u7ad9\u5305\u542b\u4e86\u6240\u6709\u7684\u8d44\u6e90\uff1aslides, nots, video, homework, project \u8fd9\u95e8\u8bfe\u7a0b\u662f CMU \u7684\u56fe\u6a21\u578b\u57fa\u7840 + \u8fdb\u9636\u8bfe\uff0c\u6388\u8bfe\u8001\u5e08\u4e3a Eric P. Xing\uff0c\u6db5\u76d6\u4e86\u56fe\u6a21\u578b\u57fa\u7840\uff0c\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u5728\u5f3a\u5316\u5b66\u4e60\u4e2d\u7684\u5e94\u7528\uff0c\u4ee5\u53ca\u975e\u53c2\u6570\u65b9\u6cd5\u3002\u76f8\u5f53\u786c\u6838","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CS229M/","text":"STATS214 / CS229M: Machine Learning Theory \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Statistics \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/stats214/ \u7ecf\u5178\u5b66\u4e60\u7406\u8bba + \u6700\u65b0\u6df1\u5ea6\u5b66\u4e60\u7406\u8bba\uff0c\u975e\u5e38\u786c\u6838\u3002\u6388\u8bfe\u8001\u5e08\u4e4b\u524d\u662f Percy Liang\uff0c\u73b0\u5728\u662f Tengyu Ma","title":"Stanford STATS214 / CS229M: Machine Learning Theory"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CS229M/#stats214-cs229m-machine-learning-theory","text":"","title":"STATS214 / CS229M: Machine Learning Theory"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CS229M/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Statistics \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/stats214/ \u7ecf\u5178\u5b66\u4e60\u7406\u8bba + \u6700\u65b0\u6df1\u5ea6\u5b66\u4e60\u7406\u8bba\uff0c\u975e\u5e38\u786c\u6838\u3002\u6388\u8bfe\u8001\u5e08\u4e4b\u524d\u662f Percy Liang\uff0c\u73b0\u5728\u662f Tengyu Ma","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STA4273/","text":"STA 4273 Winter 2021: Minimizing Expectations \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aU Toronto \u5148\u4fee\u8981\u6c42\uff1aBayesian Inference, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.cs.toronto.edu/~cmaddis/courses/sta4273_w21/ \u8fd9\u662f\u4e00\u95e8\u8f83\u4e3a\u8fdb\u9636\u7684 Ph.D. \u7814\u7a76\u8bfe\u7a0b\uff0c\u6838\u5fc3\u5185\u5bb9\u662f inference \u548c control \u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u6388\u8bfe\u8001\u5e08\u4e3a Chris Maddison (AlphaGo founding member, NeurIPS 14 best paper)","title":"U Toronto STA 4273 Winter 2021: Minimizing Expectations"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STA4273/#sta-4273-winter-2021-minimizing-expectations","text":"","title":"STA 4273 Winter 2021: Minimizing Expectations"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STA4273/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aU Toronto \u5148\u4fee\u8981\u6c42\uff1aBayesian Inference, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.cs.toronto.edu/~cmaddis/courses/sta4273_w21/ \u8fd9\u662f\u4e00\u95e8\u8f83\u4e3a\u8fdb\u9636\u7684 Ph.D. \u7814\u7a76\u8bfe\u7a0b\uff0c\u6838\u5fc3\u5185\u5bb9\u662f inference \u548c control \u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u6388\u8bfe\u8001\u5e08\u4e3a Chris Maddison (AlphaGo founding member, NeurIPS 14 best paper)","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STAT8201/","text":"Columbia STAT 8201: Deep Generative Models \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aColumbia University \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Graphical Models \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://stat.columbia.edu/~cunningham/teaching/GR8201/ \u8fd9\u95e8\u8bfe\u662f\u4e00\u95e8 PhD \u8ba8\u8bba\u73ed\uff0c\u6bcf\u5468\u7684\u5185\u5bb9\u662f\u5c55\u793a + \u8ba8\u8bba\u8bba\u6587\uff0c\u6388\u8bfe\u8001\u5e08\u662f John Cunningham\u3002Deep Generative Models \uff08\u6df1\u5ea6\u751f\u6210\u6a21\u578b\uff09 \u662f\u56fe\u6a21\u578b\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u4e5f\u662f\u73b0\u4ee3\u673a\u5668\u5b66\u4e60\u6700\u91cd\u8981\u7684\u65b9\u5411\u4e4b\u4e00","title":"Columbia STAT 8201: Deep Generative Models"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STAT8201/#columbia-stat-8201-deep-generative-models","text":"","title":"Columbia STAT 8201: Deep Generative Models"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STAT8201/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aColumbia University \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Graphical Models \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://stat.columbia.edu/~cunningham/teaching/GR8201/ \u8fd9\u95e8\u8bfe\u662f\u4e00\u95e8 PhD \u8ba8\u8bba\u73ed\uff0c\u6bcf\u5468\u7684\u5185\u5bb9\u662f\u5c55\u793a + \u8ba8\u8bba\u8bba\u6587\uff0c\u6388\u8bfe\u8001\u5e08\u662f John Cunningham\u3002Deep Generative Models \uff08\u6df1\u5ea6\u751f\u6210\u6a21\u578b\uff09 \u662f\u56fe\u6a21\u578b\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u4e5f\u662f\u73b0\u4ee3\u673a\u5668\u5b66\u4e60\u6700\u91cd\u8981\u7684\u65b9\u5411\u4e4b\u4e00","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/","text":"\u673a\u5668\u5b66\u4e60\u8fdb\u9636 \u6b64\u8def\u7ebf\u56fe\u9002\u7528\u4e8e\u5df2\u7ecf\u5b66\u8fc7\u4e86\u57fa\u7840\u673a\u5668\u5b66\u4e60 (ML, NLP, CV, RL) \u7684\u540c\u5b66 (\u9ad8\u5e74\u7ea7\u672c\u79d1\u751f\u6216\u4f4e\u5e74\u7ea7\u7814\u7a76\u751f)\uff0c\u5df2\u7ecf\u53d1\u8868\u8fc7\u81f3\u5c11\u4e00\u7bc7\u9876\u4f1a\u8bba\u6587 (NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, CVPR, ICCV) \u60f3\u8981\u8d70\u673a\u5668\u5b66\u4e60\u79d1\u7814\u8def\u7ebf\u7684\u9009\u624b\u3002 \u6b64\u8def\u7ebf\u7684\u76ee\u6807\u662f\u4e3a\u8bfb\u61c2\u4e0e\u53d1\u8868\u673a\u5668\u5b66\u4e60\u9876\u4f1a\u8bba\u6587\u6253\u4e0b\u7406\u8bba\u57fa\u7840\uff0c\u7279\u522b\u662f Probabilistic Methods \u8fd9\u4e2a track \u4e0b\u7684\u6587\u7ae0 \u673a\u5668\u5b66\u4e60\u8fdb\u9636\u53ef\u80fd\u5b58\u5728\u591a\u79cd\u4e0d\u540c\u7684\u5b66\u4e60\u8def\u7ebf\uff0c\u6b64\u8def\u7ebf\u53ea\u80fd\u4ee3\u8868\u4f5c\u8005 Yao Fu \u6240\u7406\u89e3\u7684\u6700\u4f73\u8def\u5f84\uff0c\u4fa7\u91cd\u4e8e\u8d1d\u53f6\u65af\u5b66\u6d3e\u4e0b\u7684\u6982\u7387\u5efa\u6a21\u65b9\u6cd5\uff0c\u4e5f\u4f1a\u6d89\u53ca\u5230\u5404\u9879\u76f8\u5173\u5b66\u79d1\u7684\u4ea4\u53c9\u77e5\u8bc6\u3002 \u5fc5\u8bfb\u6559\u6750 PRML: Pattern Recognition and Machine Learning. Christopher Bishop \u7ecf\u5178\u8d1d\u53f6\u65af\u5b66\u6d3e\u6559\u6750 AoS: All of Statistics. Larry Wasserman \u7ecf\u5178\u9891\u7387\u5b66\u6d3e\u6559\u6750 \u6240\u4ee5\u8fd9\u4e24\u672c\u4e66\u521a\u597d\u76f8\u8f85\u76f8\u6210 \u5b57\u5178 MLAPP: Machine Learning: A Probabilistic Perspective. Kevin Murphy Convex Optimization. Stephen Boyd and Lieven Vandenberghe \u8fdb\u9636\u4e66\u7c4d W&J: Graphical Models, Exponential Families, and Variational Inference. Martin Wainwright and Michael Jordan Theory of Point Estimation. E. L. Lehmann and George Casella \u5982\u4f55\u9605\u8bfb Guidelines \u5fc5\u8bfb\u6559\u6750\u5c31\u662f\u4e00\u5b9a\u8981\u8bfb\u7684\u6559\u6750 \u5b57\u5178\u7684\u610f\u601d\u662f\uff0c\u4e00\u822c\u60c5\u51b5\u4e0b\u4e0d\u7ba1\u5b83\uff0c\u4f46\u5f53\u9047\u5230\u4e86\u4e0d\u61c2\u7684\u6982\u5ff5\u7684\u65f6\u5019\uff0c\u5c31\u53bb\u5b57\u5178\u91cc\u9762\u67e5\uff08\u800c\u4e0d\u662f\u7ef4\u57fa\u767e\u79d1\uff09 \u8fdb\u9636\u4e66\u7c4d\u5148\u4e0d\u8bfb\uff0c\u5148\u8bfb\u5b8c\u5fc5\u8bfb\u4e66\u7c4d\u3002\u5fc5\u8bfb\u4e66\u7c4d\u4e00\u822c\u90fd\u662f\u8981\u524d\u524d\u540e\u540e\u53cd\u590d\u770b\u8fc7 N \u904d\u624d\u7b97\u8bfb\u5b8c \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u6700\u91cd\u8981\u7684\u8bfb\u6cd5\u5c31\u662f\u5bf9\u6bd4\u9605\u8bfb (contrastive-comparative reading)\uff1a\u540c\u65f6\u6253\u5f00\u4e24\u672c\u4e66\u8bb2\u540c\u4e00\u4e3b\u9898\u7684\u7ae0\u8282\uff0c\u7136\u540e\u5bf9\u6bd4\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u5c3d\u91cf\u53bb\u56de\u60f3\u4e4b\u524d\u8bfb\u8fc7\u7684\u8bba\u6587\uff0c\u6bd4\u8f83\u8bba\u6587\u548c\u6559\u6750\u7684\u76f8\u540c\u70b9\u4e0e\u4e0d\u540c\u70b9 \u57fa\u7840\u8def\u5f84 \u5148\u8bfb AoS \u7b2c\u516d\u7ae0: Models, Statistical Inference and Learning\uff0c\u8fd9\u4e00\u90e8\u5206\u662f\u6700\u57fa\u7840\u7684\u79d1\u666e \u7136\u540e\u8bfb PRML \u7b2c 10, 11 \u7ae0 \u7b2c 10 \u7ae0\u7684\u5185\u5bb9\u662f Variational Inference, \u7b2c 11 \u7ae0\u7684\u5185\u5bb9\u662f MCMC, \u8fd9\u4e24\u79cd\u65b9\u6cd5\u662f\u8d1d\u53f6\u65af\u63a8\u65ad\u7684\u4e24\u6761\u6700\u4e3b\u8981\u8def\u7ebf \u5982\u679c\u5728\u8bfb PRML \u7684\u8fc7\u7a0b\u4e2d\u53d1\u73b0\u6709\u4efb\u4f55\u4e0d\u61c2\u7684\u540d\u8bcd\uff0c\u5c31\u53bb\u7ffb\u524d\u9762\u7684\u7ae0\u8282\u3002\u5f88\u5927\u6982\u7387\u80fd\u591f\u5728\u7b2c 3\uff0c4 \u7ae0\u627e\u5230\u76f8\u5bf9\u5e94\u7684\u5b9a\u4e49\uff1b\u5982\u679c\u627e\u4e0d\u5230\u6216\u8005\u4e0d\u591f\u8be6\u7ec6\uff0c\u5c31\u53bb\u67e5 MLAPP AoS \u7b2c 8 \u7ae0 (Parametric Inference) \u548c\u7b2c 11 \u7ae0 (Bayesian Inference) \u4e5f\u53ef\u4ee5\u4f5c\u4e3a\u53c2\u8003\u3002\u6700\u597d\u7684\u65b9\u6cd5\u662f\u591a\u672c\u4e66\u5bf9\u6bd4\u9605\u8bfb\uff0c\u6d41\u7a0b\u5982\u4e0b \u5047\u8bbe\u6211\u5728\u8bfb PRML \u7b2c 10 \u7ae0\u7684\u65f6\u5019\u53d1\u73b0\u4e86\u4e00\u4e2a\u4e0d\u61c2\u7684\u8bcd\uff1aposterior inference \u4e8e\u662f\u6211\u5f80\u524d\u7ffb\uff0c\u7ffb\u5230\u4e86\u7b2c 3 \u7ae0 (Linear Model for Regression)\uff0c\u770b\u5230\u4e86\u6700\u7b80\u5355\u7684 posterior \u7136\u540e\u6211\u63a5\u7740\u7ffb AoS\uff0c\u7ffb\u5230\u4e86\u7b2c 11 \u7ae0\uff0c\u4e5f\u6709\u5bf9 posterior \u7684\u63cf\u8ff0 \u7136\u540e\u6211\u5bf9\u6bd4 PRML \u7b2c 10 \u7ae0\uff0c\u7b2c 3 \u7ae0\uff0cAoS \u7b2c 11 \u7ae0\uff0c\u4e09\u5904\u4e0d\u540c\u5730\u65b9\u5bf9 posterior \u7684\u89e3\u8bfb\uff0c\u6bd4\u8f83\u5176\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u5b8c PRML \u7b2c 10 \u548c 11 \u7ae0\u4e4b\u540e\uff0c\u63a5\u7740\u8bfb AoS \u7b2c 24 \u7ae0 (Simulation Methods)\uff0c\u7136\u540e\u628a\u5b83\u548c PRML \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb -- \u8fd9\u4fe9\u90fd\u662f\u8bb2 MCMC \u5982\u679c\u5230\u6b64\u5904\u53d1\u73b0\u8fd8\u6709\u57fa\u7840\u6982\u5ff5\u8bfb\u4e0d\u61c2\uff0c\u5c31\u56de\u5230 PRML \u7b2c 3 \u7ae0\uff0c\u628a\u5b83\u548c AoS \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb Again\uff0c\u5bf9\u6bd4\u9605\u8bfb\u975e\u5e38\u91cd\u8981\uff0c\u4e00\u5b9a\u8981\u628a\u4e0d\u540c\u672c\u4e66\u7684\u7c7b\u4f3c\u5185\u5bb9\u540c\u65f6\u6446\u5728\u9762\u524d\u76f8\u4e92\u5bf9\u6bd4\uff0c\u8fd9\u6837\u53ef\u4ee5\u663e\u8457\u589e\u5f3a\u8bb0\u5fc6 \u7136\u540e\u8bfb PRML \u7b2c 13 \u7ae0\uff08\u8df3\u8fc7\u7b2c 12 \u7ae0\uff09\uff0c\u8fd9\u4e00\u7ae0\u53ef\u4ee5\u548c MLAPP \u7684\u7b2c 17, 18 \u7ae0\u5bf9\u6bd4\u9605\u8bfb MLAPP \u7b2c 17 \u7ae0\u662f PRML \u7b2c 13.2 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 HMM MLAPP \u7b2c 18 \u7ae0\u662f PRML \u7b2c 13.3 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 LDS \u8bfb\u5b8c PRML \u7b2c 13 \u7ae0\u4e4b\u540e\uff0c\u518d\u53bb\u8bfb PRML \u7b2c 8 \u7ae0 (Graphical Models) -- \u6b64\u65f6\u8fd9\u90e8\u5206\u5e94\u8be5\u4f1a\u8bfb\u5f97\u5f88\u8f7b\u677e \u4ee5\u4e0a\u7684\u5185\u5bb9\u53ef\u4ee5\u8fdb\u4e00\u6b65\u5bf9\u7167 CMU 10-708 PGM \u8bfe\u7a0b\u6750\u6599 \u5230\u76ee\u524d\u4e3a\u6b62\uff0c\u5e94\u8be5\u80fd\u591f\u638c\u63e1 \u6982\u7387\u6a21\u578b\u7684\u57fa\u7840\u5b9a\u4e49 \u7cbe\u51c6\u63a8\u65ad - Sum-Product \u8fd1\u4f3c\u63a8\u65ad - MCMC \u8fd1\u4f3c\u63a8\u65ad - VI \u7136\u540e\u5c31\u53ef\u4ee5\u53bb\u505a\u66f4\u8fdb\u9636\u7684\u5185\u5bb9","title":"\u8fdb\u9636\u8def\u7ebf\u56fe"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_1","text":"\u6b64\u8def\u7ebf\u56fe\u9002\u7528\u4e8e\u5df2\u7ecf\u5b66\u8fc7\u4e86\u57fa\u7840\u673a\u5668\u5b66\u4e60 (ML, NLP, CV, RL) \u7684\u540c\u5b66 (\u9ad8\u5e74\u7ea7\u672c\u79d1\u751f\u6216\u4f4e\u5e74\u7ea7\u7814\u7a76\u751f)\uff0c\u5df2\u7ecf\u53d1\u8868\u8fc7\u81f3\u5c11\u4e00\u7bc7\u9876\u4f1a\u8bba\u6587 (NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, CVPR, ICCV) \u60f3\u8981\u8d70\u673a\u5668\u5b66\u4e60\u79d1\u7814\u8def\u7ebf\u7684\u9009\u624b\u3002 \u6b64\u8def\u7ebf\u7684\u76ee\u6807\u662f\u4e3a\u8bfb\u61c2\u4e0e\u53d1\u8868\u673a\u5668\u5b66\u4e60\u9876\u4f1a\u8bba\u6587\u6253\u4e0b\u7406\u8bba\u57fa\u7840\uff0c\u7279\u522b\u662f Probabilistic Methods \u8fd9\u4e2a track \u4e0b\u7684\u6587\u7ae0 \u673a\u5668\u5b66\u4e60\u8fdb\u9636\u53ef\u80fd\u5b58\u5728\u591a\u79cd\u4e0d\u540c\u7684\u5b66\u4e60\u8def\u7ebf\uff0c\u6b64\u8def\u7ebf\u53ea\u80fd\u4ee3\u8868\u4f5c\u8005 Yao Fu \u6240\u7406\u89e3\u7684\u6700\u4f73\u8def\u5f84\uff0c\u4fa7\u91cd\u4e8e\u8d1d\u53f6\u65af\u5b66\u6d3e\u4e0b\u7684\u6982\u7387\u5efa\u6a21\u65b9\u6cd5\uff0c\u4e5f\u4f1a\u6d89\u53ca\u5230\u5404\u9879\u76f8\u5173\u5b66\u79d1\u7684\u4ea4\u53c9\u77e5\u8bc6\u3002","title":"\u673a\u5668\u5b66\u4e60\u8fdb\u9636"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_2","text":"PRML: Pattern Recognition and Machine Learning. Christopher Bishop \u7ecf\u5178\u8d1d\u53f6\u65af\u5b66\u6d3e\u6559\u6750 AoS: All of Statistics. Larry Wasserman \u7ecf\u5178\u9891\u7387\u5b66\u6d3e\u6559\u6750 \u6240\u4ee5\u8fd9\u4e24\u672c\u4e66\u521a\u597d\u76f8\u8f85\u76f8\u6210","title":"\u5fc5\u8bfb\u6559\u6750"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_3","text":"MLAPP: Machine Learning: A Probabilistic Perspective. Kevin Murphy Convex Optimization. Stephen Boyd and Lieven Vandenberghe","title":"\u5b57\u5178"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_4","text":"W&J: Graphical Models, Exponential Families, and Variational Inference. Martin Wainwright and Michael Jordan Theory of Point Estimation. E. L. Lehmann and George Casella","title":"\u8fdb\u9636\u4e66\u7c4d"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_5","text":"","title":"\u5982\u4f55\u9605\u8bfb"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#guidelines","text":"\u5fc5\u8bfb\u6559\u6750\u5c31\u662f\u4e00\u5b9a\u8981\u8bfb\u7684\u6559\u6750 \u5b57\u5178\u7684\u610f\u601d\u662f\uff0c\u4e00\u822c\u60c5\u51b5\u4e0b\u4e0d\u7ba1\u5b83\uff0c\u4f46\u5f53\u9047\u5230\u4e86\u4e0d\u61c2\u7684\u6982\u5ff5\u7684\u65f6\u5019\uff0c\u5c31\u53bb\u5b57\u5178\u91cc\u9762\u67e5\uff08\u800c\u4e0d\u662f\u7ef4\u57fa\u767e\u79d1\uff09 \u8fdb\u9636\u4e66\u7c4d\u5148\u4e0d\u8bfb\uff0c\u5148\u8bfb\u5b8c\u5fc5\u8bfb\u4e66\u7c4d\u3002\u5fc5\u8bfb\u4e66\u7c4d\u4e00\u822c\u90fd\u662f\u8981\u524d\u524d\u540e\u540e\u53cd\u590d\u770b\u8fc7 N \u904d\u624d\u7b97\u8bfb\u5b8c \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u6700\u91cd\u8981\u7684\u8bfb\u6cd5\u5c31\u662f\u5bf9\u6bd4\u9605\u8bfb (contrastive-comparative reading)\uff1a\u540c\u65f6\u6253\u5f00\u4e24\u672c\u4e66\u8bb2\u540c\u4e00\u4e3b\u9898\u7684\u7ae0\u8282\uff0c\u7136\u540e\u5bf9\u6bd4\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u5c3d\u91cf\u53bb\u56de\u60f3\u4e4b\u524d\u8bfb\u8fc7\u7684\u8bba\u6587\uff0c\u6bd4\u8f83\u8bba\u6587\u548c\u6559\u6750\u7684\u76f8\u540c\u70b9\u4e0e\u4e0d\u540c\u70b9","title":"Guidelines"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_6","text":"\u5148\u8bfb AoS \u7b2c\u516d\u7ae0: Models, Statistical Inference and Learning\uff0c\u8fd9\u4e00\u90e8\u5206\u662f\u6700\u57fa\u7840\u7684\u79d1\u666e \u7136\u540e\u8bfb PRML \u7b2c 10, 11 \u7ae0 \u7b2c 10 \u7ae0\u7684\u5185\u5bb9\u662f Variational Inference, \u7b2c 11 \u7ae0\u7684\u5185\u5bb9\u662f MCMC, \u8fd9\u4e24\u79cd\u65b9\u6cd5\u662f\u8d1d\u53f6\u65af\u63a8\u65ad\u7684\u4e24\u6761\u6700\u4e3b\u8981\u8def\u7ebf \u5982\u679c\u5728\u8bfb PRML \u7684\u8fc7\u7a0b\u4e2d\u53d1\u73b0\u6709\u4efb\u4f55\u4e0d\u61c2\u7684\u540d\u8bcd\uff0c\u5c31\u53bb\u7ffb\u524d\u9762\u7684\u7ae0\u8282\u3002\u5f88\u5927\u6982\u7387\u80fd\u591f\u5728\u7b2c 3\uff0c4 \u7ae0\u627e\u5230\u76f8\u5bf9\u5e94\u7684\u5b9a\u4e49\uff1b\u5982\u679c\u627e\u4e0d\u5230\u6216\u8005\u4e0d\u591f\u8be6\u7ec6\uff0c\u5c31\u53bb\u67e5 MLAPP AoS \u7b2c 8 \u7ae0 (Parametric Inference) \u548c\u7b2c 11 \u7ae0 (Bayesian Inference) \u4e5f\u53ef\u4ee5\u4f5c\u4e3a\u53c2\u8003\u3002\u6700\u597d\u7684\u65b9\u6cd5\u662f\u591a\u672c\u4e66\u5bf9\u6bd4\u9605\u8bfb\uff0c\u6d41\u7a0b\u5982\u4e0b \u5047\u8bbe\u6211\u5728\u8bfb PRML \u7b2c 10 \u7ae0\u7684\u65f6\u5019\u53d1\u73b0\u4e86\u4e00\u4e2a\u4e0d\u61c2\u7684\u8bcd\uff1aposterior inference \u4e8e\u662f\u6211\u5f80\u524d\u7ffb\uff0c\u7ffb\u5230\u4e86\u7b2c 3 \u7ae0 (Linear Model for Regression)\uff0c\u770b\u5230\u4e86\u6700\u7b80\u5355\u7684 posterior \u7136\u540e\u6211\u63a5\u7740\u7ffb AoS\uff0c\u7ffb\u5230\u4e86\u7b2c 11 \u7ae0\uff0c\u4e5f\u6709\u5bf9 posterior \u7684\u63cf\u8ff0 \u7136\u540e\u6211\u5bf9\u6bd4 PRML \u7b2c 10 \u7ae0\uff0c\u7b2c 3 \u7ae0\uff0cAoS \u7b2c 11 \u7ae0\uff0c\u4e09\u5904\u4e0d\u540c\u5730\u65b9\u5bf9 posterior \u7684\u89e3\u8bfb\uff0c\u6bd4\u8f83\u5176\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u5b8c PRML \u7b2c 10 \u548c 11 \u7ae0\u4e4b\u540e\uff0c\u63a5\u7740\u8bfb AoS \u7b2c 24 \u7ae0 (Simulation Methods)\uff0c\u7136\u540e\u628a\u5b83\u548c PRML \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb -- \u8fd9\u4fe9\u90fd\u662f\u8bb2 MCMC \u5982\u679c\u5230\u6b64\u5904\u53d1\u73b0\u8fd8\u6709\u57fa\u7840\u6982\u5ff5\u8bfb\u4e0d\u61c2\uff0c\u5c31\u56de\u5230 PRML \u7b2c 3 \u7ae0\uff0c\u628a\u5b83\u548c AoS \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb Again\uff0c\u5bf9\u6bd4\u9605\u8bfb\u975e\u5e38\u91cd\u8981\uff0c\u4e00\u5b9a\u8981\u628a\u4e0d\u540c\u672c\u4e66\u7684\u7c7b\u4f3c\u5185\u5bb9\u540c\u65f6\u6446\u5728\u9762\u524d\u76f8\u4e92\u5bf9\u6bd4\uff0c\u8fd9\u6837\u53ef\u4ee5\u663e\u8457\u589e\u5f3a\u8bb0\u5fc6 \u7136\u540e\u8bfb PRML \u7b2c 13 \u7ae0\uff08\u8df3\u8fc7\u7b2c 12 \u7ae0\uff09\uff0c\u8fd9\u4e00\u7ae0\u53ef\u4ee5\u548c MLAPP \u7684\u7b2c 17, 18 \u7ae0\u5bf9\u6bd4\u9605\u8bfb MLAPP \u7b2c 17 \u7ae0\u662f PRML \u7b2c 13.2 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 HMM MLAPP \u7b2c 18 \u7ae0\u662f PRML \u7b2c 13.3 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 LDS \u8bfb\u5b8c PRML \u7b2c 13 \u7ae0\u4e4b\u540e\uff0c\u518d\u53bb\u8bfb PRML \u7b2c 8 \u7ae0 (Graphical Models) -- \u6b64\u65f6\u8fd9\u90e8\u5206\u5e94\u8be5\u4f1a\u8bfb\u5f97\u5f88\u8f7b\u677e \u4ee5\u4e0a\u7684\u5185\u5bb9\u53ef\u4ee5\u8fdb\u4e00\u6b65\u5bf9\u7167 CMU 10-708 PGM \u8bfe\u7a0b\u6750\u6599 \u5230\u76ee\u524d\u4e3a\u6b62\uff0c\u5e94\u8be5\u80fd\u591f\u638c\u63e1 \u6982\u7387\u6a21\u578b\u7684\u57fa\u7840\u5b9a\u4e49 \u7cbe\u51c6\u63a8\u65ad - Sum-Product \u8fd1\u4f3c\u63a8\u65ad - MCMC \u8fd1\u4f3c\u63a8\u65ad - VI \u7136\u540e\u5c31\u53ef\u4ee5\u53bb\u505a\u66f4\u8fdb\u9636\u7684\u5185\u5bb9","title":"\u57fa\u7840\u8def\u5f84"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/","text":"CS224n: Natural Language Processing \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684 NLP \u5165\u95e8\u8bfe\u7a0b\uff0c\u7531\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9886\u57df\u7684\u5de8\u4f6c Chris Manning \u9886\u8854\u6559\u6388\uff08word2vec \u7b97\u6cd5\u7684\u5f00\u521b\u8005\uff09\u3002\u5185\u5bb9\u8986\u76d6\u4e86\u8bcd\u5411\u91cf\u3001RNN\u3001LSTM\u3001Seq2Seq \u6a21\u578b\u3001\u673a\u5668\u7ffb\u8bd1\u3001\u6ce8\u610f\u529b\u673a\u5236\u3001Transformer \u7b49\u7b49 NLP \u9886\u57df\u7684\u6838\u5fc3\u77e5\u8bc6\u70b9\u3002 5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\u96be\u5ea6\u5faa\u5e8f\u6e10\u8fdb\uff0c\u5206\u522b\u662f\u8bcd\u5411\u91cf\u3001word2vec \u7b97\u6cd5\u3001Dependency parsing\u3001\u673a\u5668\u7ffb\u8bd1\u4ee5\u53ca Transformer \u7684 fine-tune\u3002 \u6700\u7ec8\u7684\u5927\u4f5c\u4e1a\u662f\u5728 Stanford \u8457\u540d\u7684 SQuAD \u6570\u636e\u96c6\u4e0a\u8bad\u7ec3 QA \u6a21\u578b\uff0c\u6709\u5b66\u751f\u7684\u5927\u4f5c\u4e1a\u751a\u81f3\u76f4\u63a5\u53d1\u8868\u4e86\u9876\u4f1a\u8bba\u6587\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs224n/index.html \u8bfe\u7a0b\u89c6\u9891\uff1aB \u7ad9\u641c\u7d22 CS224n \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224n/index.html \uff0c5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a + 1 \u4e2a Final Project \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS224n - GitHub \u4e2d\u3002","title":"Stanford CS224n: Natural Language Processing"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#cs224n-natural-language-processing","text":"","title":"CS224n: Natural Language Processing"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684 NLP \u5165\u95e8\u8bfe\u7a0b\uff0c\u7531\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9886\u57df\u7684\u5de8\u4f6c Chris Manning \u9886\u8854\u6559\u6388\uff08word2vec \u7b97\u6cd5\u7684\u5f00\u521b\u8005\uff09\u3002\u5185\u5bb9\u8986\u76d6\u4e86\u8bcd\u5411\u91cf\u3001RNN\u3001LSTM\u3001Seq2Seq \u6a21\u578b\u3001\u673a\u5668\u7ffb\u8bd1\u3001\u6ce8\u610f\u529b\u673a\u5236\u3001Transformer \u7b49\u7b49 NLP \u9886\u57df\u7684\u6838\u5fc3\u77e5\u8bc6\u70b9\u3002 5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\u96be\u5ea6\u5faa\u5e8f\u6e10\u8fdb\uff0c\u5206\u522b\u662f\u8bcd\u5411\u91cf\u3001word2vec \u7b97\u6cd5\u3001Dependency parsing\u3001\u673a\u5668\u7ffb\u8bd1\u4ee5\u53ca Transformer \u7684 fine-tune\u3002 \u6700\u7ec8\u7684\u5927\u4f5c\u4e1a\u662f\u5728 Stanford \u8457\u540d\u7684 SQuAD \u6570\u636e\u96c6\u4e0a\u8bad\u7ec3 QA \u6a21\u578b\uff0c\u6709\u5b66\u751f\u7684\u5927\u4f5c\u4e1a\u751a\u81f3\u76f4\u63a5\u53d1\u8868\u4e86\u9876\u4f1a\u8bba\u6587\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs224n/index.html \u8bfe\u7a0b\u89c6\u9891\uff1aB \u7ad9\u641c\u7d22 CS224n \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224n/index.html \uff0c5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a + 1 \u4e2a Final Project","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS224n - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/","text":"CS224w: Machine Learning with Graphs \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython, LaTeX \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684\u56fe\u795e\u7ecf\u7f51\u7edc\u5165\u95e8\u8bfe\uff0c\u8fd9\u95e8\u8bfe\u6211\u6ca1\u6709\u4e0a\u8fc7\uff0c\u4f46\u4f17\u591a\u505a GNN \u7684\u670b\u53cb\u90fd\u5411\u6211\u529b\u8350\u8fc7\u8fd9\u95e8\u8bfe\uff0c\u60f3\u5fc5 Stanford \u7684\u8bfe\u8d28\u91cf\u8fd8\u662f\u4e00\u5982\u65e2\u5f80\u5730\u6709\u4fdd\u8bc1\u7684\u3002\u53e6\u5916\u5c31\u662f\u8fd9\u95e8\u8bfe\u7684\u6388\u8bfe\u8001\u5e08\u975e\u5e38\u5e74\u8f7b\u5e05\u6c14:) \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs224w/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/watch?v=JAB_plj2rbA \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224w/ \uff0c6 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\uff0c3 \u4e2a LaTeX \u4e66\u9762\u4f5c\u4e1a","title":"Stanford CS224w: Machine Learning with Graphs"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/#cs224w-machine-learning-with-graphs","text":"","title":"CS224w: Machine Learning with Graphs"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython, LaTeX \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684\u56fe\u795e\u7ecf\u7f51\u7edc\u5165\u95e8\u8bfe\uff0c\u8fd9\u95e8\u8bfe\u6211\u6ca1\u6709\u4e0a\u8fc7\uff0c\u4f46\u4f17\u591a\u505a GNN \u7684\u670b\u53cb\u90fd\u5411\u6211\u529b\u8350\u8fc7\u8fd9\u95e8\u8bfe\uff0c\u60f3\u5fc5 Stanford \u7684\u8bfe\u8d28\u91cf\u8fd8\u662f\u4e00\u5982\u65e2\u5f80\u5730\u6709\u4fdd\u8bc1\u7684\u3002\u53e6\u5916\u5c31\u662f\u8fd9\u95e8\u8bfe\u7684\u6388\u8bfe\u8001\u5e08\u975e\u5e38\u5e74\u8f7b\u5e05\u6c14:)","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs224w/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/watch?v=JAB_plj2rbA \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224w/ \uff0c6 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\uff0c3 \u4e2a LaTeX \u4e66\u9762\u4f5c\u4e1a","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS230/","text":"Coursera: Deep Learning \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u673a\u5668\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 \u5434\u6069\u8fbe\u5728 Coursera \u5f00\u8bbe\u7684\u53e6\u4e00\u95e8\u7f51\u7ea2\u8bfe\u7a0b\uff0c\u5b66\u4e60\u8005\u65e0\u6570\uff0c\u582a\u79f0\u5723\u7ecf\u7ea7\u7684\u6df1\u5ea6\u5b66\u4e60\u5165\u95e8\u8bfe\u3002\u6df1\u5165\u6d45\u51fa\u7684\u8bb2\u89e3\uff0c\u773c\u82b1\u7f2d\u4e71\u7684 Project\u3002\u4ece\u6700\u57fa\u7840\u7684\u795e\u7ecf\u7f51\u7edc\uff0c\u5230 CNN, RNN\uff0c\u518d\u5230\u6700\u8fd1\u5927\u70ed\u7684 Transformer\u3002\u5b66\u5b8c\u8fd9\u95e8\u8bfe\uff0c\u4f60\u5c06\u521d\u6b65\u638c\u63e1\u6df1\u5ea6\u5b66\u4e60\u9886\u57df\u5fc5\u5907\u7684\u77e5\u8bc6\u548c\u6280\u80fd\uff0c\u5e76\u4e14\u53ef\u4ee5\u5728 Kaggle \u4e2d\u53c2\u52a0\u81ea\u5df1\u611f\u5174\u8da3\u7684\u6bd4\u8d5b\uff0c\u5728\u5b9e\u8df5\u4e2d\u953b\u70bc\u81ea\u5df1\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.coursera.org/specializations/deep-learning \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.coursera.org/specializations/deep-learning \uff0cB\u7ad9\u6709\u642c\u8fd0 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://www.coursera.org/specializations/deep-learning","title":"Coursera: Deep Learning"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS230/#coursera-deep-learning","text":"","title":"Coursera: Deep Learning"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS230/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u673a\u5668\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 \u5434\u6069\u8fbe\u5728 Coursera 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https://www.coursera.org/specializations/deep-learning \uff0cB\u7ad9\u6709\u642c\u8fd0 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://www.coursera.org/specializations/deep-learning","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS231/","text":"CS231n: CNN for Visual Recognition \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u673a\u5668\u5b66\u4e60\u57fa\u7840 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684 CV \u5165\u95e8\u8bfe\uff0c\u7531\u8ba1\u7b97\u673a\u9886\u57df\u7684\u5de8\u4f6c\u674e\u98de\u98de\u9662\u58eb\u9886\u8854\u6559\u6388\uff08CV \u9886\u57df\u5212\u65f6\u4ee3\u7684\u8457\u540d\u6570\u636e\u96c6 ImageNet 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Regression\u3001Classification\u3001CNN\u3001Self-Attention\u3001Transformer\u3001GAN\u3001BERT\u3001Anomaly Detection\u3001Explainable AI\u3001Attack\u3001Adaptation\u3001 RL\u3001Compression\u3001Life-Long Learning \u4ee5\u53ca Meta Learning\u3002\u53ef\u8c13\u662f\u5305\u7f57\u4e07\u8c61\uff0c\u80fd\u8ba9\u5b66\u751f\u5bf9\u4e8e\u6df1\u5ea6\u5b66\u4e60\u7684\u7edd\u5927\u591a\u6570\u9886\u57df\u90fd\u6709\u4e00\u5b9a\u4e86\u89e3\uff0c\u4ece\u800c\u53ef\u4ee5\u8fdb\u4e00\u6b65\u9009\u62e9\u60f3\u8981\u6df1\u5165\u7684\u65b9\u5411\u8fdb\u884c\u5b66\u4e60\u3002 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\uff0c\u6bcf\u8282\u8bfe\u7684\u94fe\u63a5\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://speech.ee.ntu.edu.tw/~hylee/ml/2021-spring.html \uff0c15 \u4e2a lab\uff0c\u51e0\u4e4e\u8986\u76d6\u4e86\u4e3b\u6d41\u6df1\u5ea6\u5b66\u4e60\u7684\u6240\u6709\u9886\u57df","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/EE16/","text":"UCB EE16A&B: Designing Information Devices and Systems I&II Descriptions Offered by: UC Berkeley Prerequisites: None Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 150 hours This introductory class for freshmen majoring in electronics at UC Berkeley teaches the fundamentals of circuitry. 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Course Resources Course Website: EE16A , EE16B Recordings: EE16A , EE16B Textbooks: EE16A , EE16B Assignments: EE16A , EE16B Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/EE16A - GitHub .","title":"EE16A&B: Designing Information Devices and Systems I&II"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/EE16/#ucb-ee16ab-designing-information-devices-and-systems-iii","text":"","title":"UCB EE16A&B: Designing Information Devices and Systems I&II"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/EE16/#descriptions","text":"Offered by: UC Berkeley Prerequisites: None Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 150 hours This introductory class for freshmen majoring in electronics at UC Berkeley teaches the fundamentals of circuitry. Through a variety of hands-on labs, students will experience collecting information from the environment through sensors and analyzing it to make predictions. 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Oppenheim Okay, enough reason to take this class.","title":"Descriptions"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/Signals_and_Systems_AVO/#course-resources","text":"Course Website: https://ocw.mit.edu/resources/res-6-007-signals-and-systems-spring-2011/index.htm Recordings: https://www.bilibili.com/video/BV1CZ4y1j7hs Textbooks: Signals and Systems, 2nd Edition Assignments: https://ocw.mit.edu/resources/res-6-007-signals-and-systems-spring-2011/assignments","title":"Course Resources"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/signal/","text":"UCB EE120: Signal and Systems \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A, CS70\uff0c\u5fae\u79ef\u5206\uff0c\u7ebf\u6027\u4ee3\u6570 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u6700\u7cbe\u534e\u7684\u90e8\u5206\u5c31\u662f 6 \u4e2a\u8d85\u6709\u8da3\u7684\u7f16\u7a0b\u4f5c\u4e1a\u4e86\uff0c\u4f1a\u8ba9\u4f60\u7528 Python \u901a\u8fc7\u5b66\u4e60\u5230\u7684\u4fe1\u53f7\u4e0e\u7cfb\u7edf\u7684\u7406\u8bba\u77e5\u8bc6\uff0c\u89e3\u51b3\u5404\u7c7b\u5b9e\u9645\u95ee\u9898\u3002\u4f8b\u5982 lab3 \u4f1a\u8ba9\u4f60\u5b9e\u73b0 FFT \u7b97\u6cd5\uff0c\u5e76\u548c Numpy \u7684\u5b98\u65b9\u5b9e\u73b0\u8fdb\u884c\u6027\u80fd\u5bf9\u6bd4\uff1blab4 \u4f1a\u901a\u8fc7\u5206\u6790\u624b\u6307\u5934\u7684\u5f71\u50cf\u6570\u636e\u63a8\u65ad\u5fc3\u7387\uff1blab5 \u5c31\u66f4\u725b\u4e86\uff0c\u4f1a\u8ba9\u4f60\u7ed9\u54c8\u52c3\u671b\u8fdc\u955c\u62cd\u5230\u7684\u7167\u7247\u8fdb\u884c\u964d\u566a\u5904\u7406\uff0c\u6062\u590d\u7eda\u70c2\u6e05\u6670\u7684\u661f\u7a7a\uff1blab6 \u4f1a\u8ba9\u4f60\u6784\u9020\u4e00\u4e2a\u53cd\u9988\u7cfb\u7edf\uff0c\u5e73\u8861\u5c0f\u8f66\u4e0a\u7684\u7ec6\u6746\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://inst.eecs.berkeley.edu/~ee120/fa19/ \u8bfe\u7a0b\u6559\u6750\uff1a\u53c2\u89c1\u8bfe\u7a0b notes \u8bfe\u7a0b\u4f5c\u4e1a\uff1a5 \u4e2a\u4e66\u9762\u4f5c\u4e1a + 6 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/UCB-EE120 - 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GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E7%A8%8B%E5%BA%8F%E8%AF%AD%E8%A8%80%E8%AE%BE%E8%AE%A1/CS242/","text":"","title":"CS242"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/","text":"UCB CS161: Computer Security \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A, CS61B, CS61C \u7f16\u7a0b\u8bed\u8a00\uff1aC, Go \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 \u4f2f\u514b\u5229\u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u8bfe\u7a0b\u5185\u5bb9\u5206\u4e3a5\u4e2a\u90e8\u5206\uff1a Security principles: how to design a secure system Memory safety: buffer overflow attack Cryptography: symmetric encryption, asymmetric encryption, MAC, digital signature ......... Web: SQL-injection, XSS, XSRF ....... Networking: attacks for each layer \u8fd9\u95e8\u8bfe\u8ba9\u6211\u5370\u8c61\u6700\u4e3a\u6df1\u523b\u7684\u90e8\u5206\u662f Project2\uff0c\u8ba9\u4f60\u7528 Go \u8bed\u8a00\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7684\u6587\u4ef6\u5206\u4eab\u7cfb\u7edf\u3002\u6211\u82b1\u4e86\u6574\u6574\u4e09\u5929\u624d\u5b8c\u6210\u4e86\u8fd9\u4e2a\u975e\u5e38\u8650\u7684 Project\uff0c\u603b\u4ee3\u7801\u91cf\u8d85\u8fc7 3k \u884c\u3002\u5728\u8fd9\u6837\u5bc6\u96c6\u578b\u7684\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u80fd\u6781\u5927\u5730\u953b\u70bc\u4f60\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7cfb\u7edf\u7684\u80fd\u529b\u3002 2020 \u5e74\u590f\u5b63\u5b66\u671f\u7684\u7248\u672c\u5f00\u6e90\u4e86\u8bfe\u7a0b\u5f55\u5f71\uff0c\u5927\u5bb6\u53ef\u4ee5\u5728\u4e0b\u9762\u7684\u8bfe\u7a0b\u7f51\u7ad9\u94fe\u63a5\u91cc\u627e\u5230\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://su20.cs161.org/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a https://textbook.cs161.org/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a7 \u4e2a\u5728\u7ebf HW + 3 \u4e2a Lab + 3 \u4e2a Project \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/UCB-CS161 - GitHub \u4e2d\u3002","title":"UCB CS161: Computer Security"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#ucb-cs161-computer-security","text":"","title":"UCB CS161: Computer Security"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A, CS61B, CS61C \u7f16\u7a0b\u8bed\u8a00\uff1aC, Go \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 \u4f2f\u514b\u5229\u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u8bfe\u7a0b\u5185\u5bb9\u5206\u4e3a5\u4e2a\u90e8\u5206\uff1a Security principles: how to design a secure system Memory safety: buffer overflow attack Cryptography: symmetric encryption, asymmetric encryption, MAC, digital signature ......... Web: SQL-injection, XSS, XSRF ....... Networking: attacks for each layer \u8fd9\u95e8\u8bfe\u8ba9\u6211\u5370\u8c61\u6700\u4e3a\u6df1\u523b\u7684\u90e8\u5206\u662f Project2\uff0c\u8ba9\u4f60\u7528 Go \u8bed\u8a00\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7684\u6587\u4ef6\u5206\u4eab\u7cfb\u7edf\u3002\u6211\u82b1\u4e86\u6574\u6574\u4e09\u5929\u624d\u5b8c\u6210\u4e86\u8fd9\u4e2a\u975e\u5e38\u8650\u7684 Project\uff0c\u603b\u4ee3\u7801\u91cf\u8d85\u8fc7 3k \u884c\u3002\u5728\u8fd9\u6837\u5bc6\u96c6\u578b\u7684\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u80fd\u6781\u5927\u5730\u953b\u70bc\u4f60\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7cfb\u7edf\u7684\u80fd\u529b\u3002 2020 \u5e74\u590f\u5b63\u5b66\u671f\u7684\u7248\u672c\u5f00\u6e90\u4e86\u8bfe\u7a0b\u5f55\u5f71\uff0c\u5927\u5bb6\u53ef\u4ee5\u5728\u4e0b\u9762\u7684\u8bfe\u7a0b\u7f51\u7ad9\u94fe\u63a5\u91cc\u627e\u5230\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://su20.cs161.org/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a https://textbook.cs161.org/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a7 \u4e2a\u5728\u7ebf HW + 3 \u4e2a Lab + 3 \u4e2a Project","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/UCB-CS161 - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/","text":"MIT 6.858: Computer System Security \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\uff0c\u5bf9\u8ba1\u7b97\u673a\u7cfb\u7edf\u6709\u521d\u6b65\u4e86\u89e3 \u7f16\u7a0b\u8bed\u8a00\uff1aC, Python \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 MIT \u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u5b9e\u9a8c\u73af\u5883\u662f\u4e00\u4e2a Web Application Zoobar\u3002\u5b66\u751f\u5b66\u4e60\u653b\u9632\u6280\u672f\u5e76\u5e94\u7528\u4e8e\u8be5 Web Application\u3002 Lab 1: you will explore the zoobar web application, and use buffer overflow attacks to break its security properties. Lab 2: you will improve the zoobar web application by using privilege separation, so that if one component is compromised, the adversary doesn't get control over the whole web application. Lab 3: you will build a program analysis tool based on symbolic execution to find bugs in Python code such as the zoobar web application. Lab 4: you will improve the zoobar application against browser attacks. \u8fd9\u4e2a\u8bfe\u6211\u4e3b\u8981\u662f\u505a\u4e86 Lab 3\u3002Lab 3 \u662f\u901a\u8fc7\u6df7\u5408\u7b26\u53f7\u6267\u884c\u6765\u904d\u5386\u7a0b\u5e8f\u7684\u6240\u6709\u5206\u652f\uff0c\u7406\u89e3\u4e86\u7b26\u53f7\u6267\u884c\u7684\u601d\u60f3\u540e Lab \u5e76\u4e0d\u96be\u505a\u3002\u8fd9\u4e2a Lab \u76f4\u89c2\u5c55\u793a\u7b26\u53f7\u6267\u884c\u8fd9\u79cd\u6280\u672f\u7684\u4f7f\u7528\u65b9\u6cd5\u3002 \u8fd9\u4e2a\u8bfe\u7684 Final Project \u662f\u5b9e\u73b0 SecFS \uff0c\u4e00\u4e2a\u8fdc\u7aef\u6587\u4ef6\u7cfb\u7edf\uff0c\u9762\u5bf9\u5b8c\u5168\u4e0d\u53ef\u4fe1\u7684\u670d\u52a1\u5668\uff0c\u63d0\u4f9b\u673a\u5bc6\u6027\u548c\u5b8c\u6574\u6027\u3002\u53c2\u8003\u8bba\u6587\u4e3a SUNDR \u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://css.csail.mit.edu/6.858/2022/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a4 \u4e2a Lab + Final Project / Lab5","title":"MIT 6.858: Computer System Security"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/#mit-6858-computer-system-security","text":"","title":"MIT 6.858: Computer System Security"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\uff0c\u5bf9\u8ba1\u7b97\u673a\u7cfb\u7edf\u6709\u521d\u6b65\u4e86\u89e3 \u7f16\u7a0b\u8bed\u8a00\uff1aC, Python \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 MIT \u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u5b9e\u9a8c\u73af\u5883\u662f\u4e00\u4e2a Web Application Zoobar\u3002\u5b66\u751f\u5b66\u4e60\u653b\u9632\u6280\u672f\u5e76\u5e94\u7528\u4e8e\u8be5 Web Application\u3002 Lab 1: you will explore the zoobar web application, and use buffer overflow attacks to break its security properties. Lab 2: you will improve the zoobar web application by using privilege separation, so that if one component is compromised, the adversary doesn't get control over the whole web application. Lab 3: you will build a program analysis tool based on symbolic execution to find bugs in Python code such as the zoobar web application. Lab 4: you will improve the zoobar application against browser attacks. \u8fd9\u4e2a\u8bfe\u6211\u4e3b\u8981\u662f\u505a\u4e86 Lab 3\u3002Lab 3 \u662f\u901a\u8fc7\u6df7\u5408\u7b26\u53f7\u6267\u884c\u6765\u904d\u5386\u7a0b\u5e8f\u7684\u6240\u6709\u5206\u652f\uff0c\u7406\u89e3\u4e86\u7b26\u53f7\u6267\u884c\u7684\u601d\u60f3\u540e Lab \u5e76\u4e0d\u96be\u505a\u3002\u8fd9\u4e2a Lab \u76f4\u89c2\u5c55\u793a\u7b26\u53f7\u6267\u884c\u8fd9\u79cd\u6280\u672f\u7684\u4f7f\u7528\u65b9\u6cd5\u3002 \u8fd9\u4e2a\u8bfe\u7684 Final Project \u662f\u5b9e\u73b0 SecFS \uff0c\u4e00\u4e2a\u8fdc\u7aef\u6587\u4ef6\u7cfb\u7edf\uff0c\u9762\u5bf9\u5b8c\u5168\u4e0d\u53ef\u4fe1\u7684\u670d\u52a1\u5668\uff0c\u63d0\u4f9b\u673a\u5bc6\u6027\u548c\u5b8c\u6574\u6027\u3002\u53c2\u8003\u8bba\u6587\u4e3a SUNDR \u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://css.csail.mit.edu/6.858/2022/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a4 \u4e2a Lab + Final Project / Lab5","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/","text":"Stanford CS106B/X: Programming Abstractions in C++ Descriptions Offered by: Stanford Prerequisites: CS50/CS106A/CS61A or equivalent Programming Languages: C++ Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 50-70 hours CS106B/X are advanced programming courses at Stanford. CS106X is more difficult and in-depth than CS106B, but the main content is similar. Based on programming assignments in C++ language, students will develop the ability to solve real-world problems through programming abstraction. It also covers some simple data structures and algorithms, but is generally not as systematic as a specialized data structures course. Resources Course Website: CS106B , CS106X Textbook: https://web.stanford.edu/class/cs106x/res/reader/CS106BX-Reader.pdf Recordings: https://www.bilibili.com/video/BV1G7411k7jG","title":"Stanford CS106B/X"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/#stanford-cs106bx-programming-abstractions-in-c","text":"","title":"Stanford CS106B/X: Programming Abstractions in C++"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/#descriptions","text":"Offered by: Stanford Prerequisites: CS50/CS106A/CS61A or equivalent Programming Languages: C++ Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 50-70 hours CS106B/X are advanced programming courses at Stanford. CS106X is more difficult and in-depth than CS106B, but the main content is similar. Based on programming assignments in C++ language, students will develop the ability to solve real-world problems through programming abstraction. It also covers some simple data structures and algorithms, but is generally not as systematic as a specialized data structures course.","title":"Descriptions"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/#resources","text":"Course Website: CS106B , CS106X Textbook: https://web.stanford.edu/class/cs106x/res/reader/CS106BX-Reader.pdf Recordings: https://www.bilibili.com/video/BV1G7411k7jG","title":"Resources"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/","text":"CS106L: Stanford C++ Programming Descriptions Offered by: Stanford Prerequisites: better if you are already proficient in a programming language Programming Languages: C++ Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 20 hours I've been writing C++ code since freshman year, and it wasn't until I finished this class that I realized the C++ code I was writing was probably just C + cin / cout . This class will dive into a lot of standard C++ features and syntax that will allow you to write quality C++ code. Techniques such as auto binding, uniform initialization, lambda function, move semantics, RAII, etc. have been used repeatedly in my coding career since then and are very useful. It is worth mentioning that in this class, you will implement a HashMap (similar to unordered_map in STL), which almost ties the whole course together and is a great test of coding skills. Especially after the implementation of iterator , I started to understand why Linus is so sarcastic about C/C++, because it is really hard to write correctly. In short, the course is not difficult but very informative which requires you to consolidate repeatedly in later practice. The reason why Stanford offers a single C++ programming class is that many of the subsequent CS courses' projects are based on C++. For example, CS144 Computer Networks and CS143 Compilers. Both of these courses are included in this book. Course Resources Course Website: http://web.stanford.edu/class/cs106l/ Recordings: https://www.youtube.com/channel/UCSqr6y-eaQT_qZJVUm_4QxQ/playlists Textbook: http://web.stanford.edu/class/cs106l/full_course_reader.pdf Assignment1 Download: https://github.com/snme/cs106L-assignment1 Assignment2 Download: https://github.com/snme/cs106L-assignment2 Assignments: refer to course website Personal Resources All the resources and assignments used by @PKUFlyingPig are maintained in PKUFlyingPig/CS106L - GitHub .","title":"Stanford CS106L: Standard C++ Programming"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#cs106l-stanford-c-programming","text":"","title":"CS106L: Stanford C++ Programming"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#descriptions","text":"Offered by: Stanford Prerequisites: better if you are already proficient in a programming language Programming Languages: C++ Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 20 hours I've been writing C++ code since freshman year, and it wasn't until I finished this class that I realized the C++ code I was writing was probably just C + cin / cout . This class will dive into a lot of standard C++ features and syntax that will allow you to write quality C++ code. Techniques such as auto binding, uniform initialization, lambda function, move semantics, RAII, etc. have been used repeatedly in my coding career since then and are very useful. It is worth mentioning that in this class, you will implement a HashMap (similar to unordered_map in STL), which almost ties the whole course together and is a great test of coding skills. Especially after the implementation of iterator , I started to understand why Linus is so sarcastic about C/C++, because it is really hard to write correctly. In short, the course is not difficult but very informative which requires you to consolidate repeatedly in later practice. The reason why Stanford offers a single C++ programming class is that many of the subsequent CS courses' projects are based on C++. For example, CS144 Computer Networks and CS143 Compilers. Both of these courses are included in this book.","title":"Descriptions"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#course-resources","text":"Course Website: http://web.stanford.edu/class/cs106l/ Recordings: https://www.youtube.com/channel/UCSqr6y-eaQT_qZJVUm_4QxQ/playlists Textbook: http://web.stanford.edu/class/cs106l/full_course_reader.pdf Assignment1 Download: https://github.com/snme/cs106L-assignment1 Assignment2 Download: https://github.com/snme/cs106L-assignment2 Assignments: refer to course website","title":"Course Resources"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig are maintained in PKUFlyingPig/CS106L - GitHub .","title":"Personal Resources"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS110L/","text":"CS110L: Safety in Systems Programming \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6700\u597d\u6709\u4e00\u5b9a\u7684\u7f16\u7a0b\u80cc\u666f\u5e76\u5bf9\u8ba1\u7b97\u673a\u7cfb\u7edf\u6709\u521d\u6b65\u7684\u8ba4\u8bc6\u3002 \u7f16\u7a0b\u8bed\u8a00\uff1aRust \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a30 \u5c0f\u65f6 \u5728\u8fd9\u95e8\u8bfe\u4e2d\u4f60\u5c06\u4f1a\u5b66\u4e60 Rust \u8fd9\u95e8\u795e\u5947\u7684\u8bed\u8a00\u3002 \u5982\u679c\u4f60\u5b66\u8fc7 C \u5e76\u63a5\u89e6\u8fc7\u4e00\u4e9b\u7cfb\u7edf\u7f16\u7a0b\u7684\u8bdd\uff0c\u5e94\u8be5\u5bf9 C \u7684\u5185\u5b58\u6cc4\u6f0f\u4ee5\u53ca\u6307\u9488\u7684\u5371\u9669\u6709\u6240\u8033\u95fb\uff0c\u4f46 C \u7684\u5e95\u5c42\u7279\u6027\u4ee5\u53ca\u9ad8\u6548\u4ecd\u7136\u8ba9\u5b83\u5728\u7cfb\u7edf\u7ea7\u7f16\u7a0b\u4e2d\u65e0\u6cd5\u88ab\u4f8b\u5982 Java \u7b49\u81ea\u5e26\u5783\u573e\u6536\u96c6\u673a\u5236\u7684\u9ad8\u7ea7\u8bed\u8a00\u6240\u66ff\u4ee3\u3002\u800c Rust \u7684\u76ee\u6807\u5219\u662f\u5e0c\u671b\u5728 C \u7684\u9ad8\u6548\u57fa\u7840\u4e0a\uff0c\u5f25\u8865\u5176\u5b89\u5168\u4e0d\u8db3\u7684\u7f3a\u70b9\u3002\u56e0\u6b64 Rust \u5728\u8bbe\u8ba1\u4e4b\u521d\uff0c\u5c31\u6709\u5e26\u6709\u5f88\u591a\u7cfb\u7edf\u7f16\u7a0b\u7684\u89c2\u70b9\u3002\u5b66\u4e60 Rust\uff0c\u4e5f\u80fd\u8ba9\u4f60\u4e4b\u540e\u80fd\u7528 C \u8bed\u8a00\u7f16\u5199\u51fa\u66f4\u5b89\u5168\u66f4\u4f18\u96c5\u7684\u7cfb\u7edf\u7ea7\u4ee3\u7801\uff08\u4f8b\u5982\u64cd\u4f5c\u7cfb\u7edf\u7b49\uff09\u3002 \u8fd9\u95e8\u8bfe\u7684\u540e\u534a\u90e8\u5206\u5173\u6ce8\u5728\u5e76\u53d1\uff08concurrency\uff09\u8fd9\u4e00\u4e3b\u9898\u4e0a\uff0c\u4f60\u5c06\u4f1a\u7cfb\u7edf\u5730\u638c\u63e1\u591a\u8fdb\u7a0b\u3001\u591a\u7ebf\u7a0b\u3001\u57fa\u4e8e\u4e8b\u4ef6\u9a71\u52a8\u7684\u5e76\u53d1\u7b49\u82e5\u5e72\u5e76\u53d1\u6280\u672f\uff0c\u5e76\u5728\u7b2c\u4e8c\u4e2a Project \u4e2d\u6bd4\u8f83\u5b83\u4eec\u5404\u81ea\u7684\u4f18\u52a3\u3002Rust \u4e2d \u201cfutures\u201d \u7684\u6982\u5ff5\u975e\u5e38\u6709\u8da3\u548c\u4f18\u96c5\uff0c\u8fd9\u4e9b\u57fa\u7840\u77e5\u8bc6\u5bf9\u4f60\u540e\u7eed\u5bf9\u8ba1\u7b97\u673a\u7cfb\u7edf\u76f8\u5173\u8bfe\u7a0b\u7684\u5b66\u4e60\u5f88\u6709\u5e2e\u52a9\u3002\u53e6\u5916\uff0c\u6e05\u534e\u5927\u5b66\u7684\u64cd\u7edf\u5b9e\u9a8c rCore \u5c31\u662f\u57fa\u4e8e Rust \u7f16\u5199\u7684\uff0c\u5177\u4f53\u53c2\u89c1 \u6587\u6863 \u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://reberhardt.com/cs110l/spring-2020/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://youtu.be/j7AQrtLevUE \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u5171 6 \u4e2a Lab \u548c 2 \u4e2a Project\uff0c\u4f5c\u4e1a\u6587\u6863\u548c\u4ee3\u7801\u6846\u67b6\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9\u3002\u5176\u4e2d\u4e24\u4e2a Project \u975e\u5e38\u6709\u8da3\uff0c\u5206\u522b\u662f\uff1a \u7528 Rust \u5b9e\u73b0\u4e00\u4e2a\u7c7b\u4f3c\u4e8e GDB \u7684 debugger \u7528 Rust \u5b9e\u73b0\u4e00\u4e2a\u8d1f\u8f7d\u5747\u8861\u5668 \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS110L - 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GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS50/","text":"CS50: This is CS50x Descriptions Offered by: Harvard Prerequisites: None Programming Languages: C, Python, SQL, HTML, CSS, JavaScript Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 20 hours This course has been voted the most popular public course by Harvard students for many years. Professor Malan is very passionate in class. I still remember the scene where he tears up the Yellow pages to explain the dichotomy method. Since this is a university-wide public course, the contents are pretty friendly to beginners and even if you already have some programming experience, all the programming assignments are quite exciting and worth a try. Course Resources Course Website: https://cs50.harvard.edu/x/2022/ Recordings: https://cs50.harvard.edu/x/2022/ Assignments: https://cs50.harvard.edu/x/2022/","title":"Harvard CS50: This is CS50x"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS50/#cs50-this-is-cs50x","text":"","title":"CS50: This is CS50x"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS50/#descriptions","text":"Offered by: Harvard Prerequisites: None Programming Languages: C, Python, SQL, HTML, CSS, JavaScript Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 20 hours This course has been voted the most popular public course by Harvard students for many years. Professor Malan is very passionate in class. I still remember the scene where he tears up the Yellow pages to explain the dichotomy method. 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The CS61 series is composed of introductory courses to the CS major at Berkeley, where CS61A: Emphasizes abstraction and equips students to use programs to solve real-world problems without focusing on the underlying hardware details. CS61B: Focuses on algorithms and data structures and the construction of large-scale programs, where students combine knowledge of algorithms and data structures with the Java language to build large-scale projects at the thousand-line code level (such as a simple Google Maps, a two-dimensional version of Minecraft). CS61C: Focusing on computer architecture, students will understand how high-level languages (e.g. C) are converted step-by-step into machine-understandable bit strings and executed on CPUs. Students will learn about the RISC-V architecture and implement a CPU on their own by using Logism. CS61B and CS61C are both included in this guidebook. Going back to CS61A, you will note that this is not just a programming language class, but goes deeper into the principles of program construction and operation. Finally you will implement an interpreter for Scheme in Python in Project 4. In addition, abstraction will be a major theme in this class, as you will learn about functional programming, data abstraction, object orientation, etc. to make your code more readable and modular. Of course, learning a programming language is also a big part of this course. You will master three programming languages, Python, Scheme, and SQL, and in learning and comparing them, you will be equiped with the ability to quickly master a new programming language. Note: If you have no prior programming experience at all, getting started with CS61A requires a relatively high level of learning ability and self-discipline. To avoid the frustration of a struggling experience, you may choose a more friendly introductory programming course at first. For example, CS10 at Berkeley or CS50 at Harvard are both good choices. Course Resources Course Website: https://inst.eecs.berkeley.edu/~cs61a/su20/ Recordings: refer to the course website Textbook: http://composingprograms.com/ Assignments: refer to the course website Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPic/CS61A - GitHub","title":"UCB CS61A: Structure and Interpretation of Computer Programs"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS61A/#cs61a-structure-and-interpretation-of-computer-programs","text":"","title":"CS61A: Structure and Interpretation of Computer Programs"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS61A/#descriptions","text":"Offered by: UC Berkeley Prerequisites: None Programming Languages: Python, Scheme, SQL Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 50 hours This is the first course in the Berkeley CS61 series, and my introductory course to Python. The CS61 series is composed of introductory courses to the CS major at Berkeley, where CS61A: Emphasizes abstraction and equips students to use programs to solve real-world problems without focusing on the underlying hardware details. CS61B: Focuses on algorithms and data structures and the construction of large-scale programs, where students combine knowledge of algorithms and data structures with the Java language to build large-scale projects at the thousand-line code level (such as a simple Google Maps, a two-dimensional version of Minecraft). CS61C: Focusing on computer architecture, students will understand how high-level languages (e.g. C) are converted step-by-step into machine-understandable bit strings and executed on CPUs. Students will learn about the RISC-V architecture and implement a CPU on their own by using Logism. CS61B and CS61C are both included in this guidebook. Going back to CS61A, you will note that this is not just a programming language class, but goes deeper into the principles of program construction and operation. Finally you will implement an interpreter for Scheme in Python in Project 4. In addition, abstraction will be a major theme in this class, as you will learn about functional programming, data abstraction, object orientation, etc. to make your code more readable and modular. Of course, learning a programming language is also a big part of this course. You will master three programming languages, Python, Scheme, and SQL, and in learning and comparing them, you will be equiped with the ability to quickly master a new programming language. Note: If you have no prior programming experience at all, getting started with CS61A requires a relatively high level of learning ability and self-discipline. To avoid the frustration of a struggling experience, you may choose a more friendly introductory programming course at first. For example, CS10 at Berkeley or CS50 at Harvard are both good choices.","title":"Descriptions"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS61A/#course-resources","text":"Course Website: https://inst.eecs.berkeley.edu/~cs61a/su20/ Recordings: refer to the course website Textbook: http://composingprograms.com/ Assignments: refer to the course website","title":"Course Resources"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS61A/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPic/CS61A - GitHub","title":"Personal Resources"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/Duke-Coursera-Intro-C/","text":"Introductory C Programming Specialization \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aDuke \u5148\u4fee\u8981\u6c42\uff1a\u65e0 \u7f16\u7a0b\u8bed\u8a00\uff1aC \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a110 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Specialization"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/Duke-Coursera-Intro-C/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aDuke \u5148\u4fee\u8981\u6c42\uff1a\u65e0 \u7f16\u7a0b\u8bed\u8a00\uff1aC \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a110 \u5c0f\u65f6 \u975e\u5e38\u597d\u7684\u8bfe\u7a0b\uff0c\u81ea\u6211\u611f\u89c9\u6536\u76ca\u975e\u5e38\u5927\uff1a \u4fa7\u91cd\u57fa\u7840\u548c\u57fa\u672c\u6982\u5ff5\uff1a\u5982 frame\u3001stack memory\u3001heap memory \u7b49\u8bb2\u5f97\u5f88\u900f\u3002 \u9488\u5bf9C\u6700\u96be\u638c\u63e1\u7684\u6307\u9488\uff0c\u6709\u597d\u7684\u7ec3\u4e60\u548c\u7f16\u7a0b\u6765\u52a0\u6df1\u548c\u5f3a\u5316\u7406\u89e3\u3002 \u975e\u5e38\u597d\u7684 GDB\uff0cValgrind \u4e0a\u624b\u8bad\u7ec3\uff0c\u4f5c\u4e1a\u4e5f\u4f1a\u6d89\u53ca\u4e00\u4e9b\u57fa\u672c\u7684 Git \u7ec3\u4e60\u3002 \u8001\u5e08\u5efa\u8bae\u4f5c\u4e1a\u7528 Emacs\uff0c\u6240\u4ee5\u5bf9 Emacs \u5c0f\u767d\u6765\u8bf4\uff0c\u662f\u4e2a\u4e0d\u9519\u7684\u5165\u95e8\u3002\u5982\u679c\u4f60\u4f1a\u7528 Vim \uff0c\u6211\u5efa\u8bae\u4f60\u7528 Evil \u63d2\u4ef6\u3002\u8fd9\u6837\u4f60\u4e0d\u4f1a\u4e22\u6389 Vim \u7684\u7f16\u8f91\u529f\u80fd\uff0c\u540c\u65f6\u53ef\u4ee5\u4f53\u4f1a Emacs \u7684\u5f3a\u5927\u3002\u5de5\u5177\u7bb1\u91cc\u540c\u65f6\u6709 Emacs \u548c Vim \u65f6\uff0c\u6548\u7387\u4f1a\u6709\u4e0d\u5c11\u63d0\u9ad8\u3002Emacs \u7684 org-mode\uff0c\u548c GDB \u7684\u987a\u6ed1\u6574\u5408\uff0c\u7b49\u7b49\u7b49\u7b49\uff0c\u90fd\u4f1a\u8ba9\u4f60\u5982\u864e\u6dfb\u7ffc\u3002 \u867d\u7136\u53ef\u80fd\u9700\u8981\u4ed8\u8d39\uff0c\u4f46\u6211\u89c9\u5f97\u503c\u3002 Coursera \u628a\u8fd9\u4e00\u5927\u8bfe\u5206\u6210\u4e86\u56db\u4e2a\u5c0f\u8bfe\uff0c\u4f46\u5c0f\u8bfe\u4e4b\u95f4\u7684\u4f5c\u4e1a\u6587\u4ef6\u7684\u8f6c\u79fb\u5e76\u4e0d\u987a\u5229\u3002\u8fd9\u662f\u4e2a\u6709\u70b9\u8ba8\u538c\u7684\u5730\u65b9\u3002\u5e78\u8fd0\u7684\u662f\uff0c\u540e\u9762\u7684\u5c0f\u8bfe\u53ef\u4ee5\u7ed9\u4ee5\u524d\u5c0f\u8bfe\u7684\u4f5c\u4e1a\u6253\u5206\uff0c\u6240\u4ee5\u6211\u662f\u63a5\u7740\u628a\u524d\u9762\u7684\u4f5c\u4e1a\u53c8\u505a\u4e86\u4e00\u904d\u3002\u6743\u5f53\u5de9\u56fa\uff0c\u6548\u679c\u4e0d\u9519\u3002 \u867d\u8bf4\u8bfe\u540d\u662f\u5165\u95e8\uff0c\u4f46\u517c\u5177\u5e7f\u5ea6\u548c\u6df1\u5ea6\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/Duke-Coursera-Intro-C/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.coursera.org/specializations/c-programming \u8bfe\u7a0b\u89c6\u9891\uff1a\u540c\u4e0a \u8bfe\u7a0b\u6559\u6750\uff1a\u540c\u4e0a \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u540c\u4e0a","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/Duke-Coursera-Intro-C/#_3","text":"@haidongji \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7684\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 Duke Coursera Intro C \u4e2d\u3002\u56e0\u4e3a\u65f6\u95f4\u5173\u7cfb\uff0c\u6211\u6700\u540e\u4e00\u8bfe\u6700\u540e\u4e00\u5468\u7684\u51e0\u4e2a\u4f5c\u4e1a\u5230\u76ee\u524d\u8fd8\u6ca1\u6709\u5b8c\u6210\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/MIT-Missing-Semester/","text":"MIT: The Missing Semester of Your CS Education Descriptions Offered by: MIT Prerequisites: None Programming Languages: Shell Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 10 hours Just as the course name indicated, this course will teach the missing things in the university courses. It will cover shell programming, git, vim editor, tmux, ssh, sed, awk and even how to beautify your terminal. Trust me, this will be your first step to become a hacker! Resources Homepage: https://missing.csail.mit.edu/ Records: https://www.youtube.com/playlist?list=PLyzOVJj3bHQuloKGG59rS43e29ro7I57J Assignments: Some exercises after each lecture.","title":"MIT-Missing-Semester"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/MIT-Missing-Semester/#mit-the-missing-semester-of-your-cs-education","text":"","title":"MIT: The Missing Semester of Your CS Education"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/MIT-Missing-Semester/#descriptions","text":"Offered by: MIT Prerequisites: None Programming Languages: Shell Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 10 hours Just as the course name indicated, this course will teach the missing things in the university courses. It will cover shell programming, git, vim editor, tmux, ssh, sed, awk and even how to beautify your terminal. Trust me, this will be your first step to become a hacker!","title":"Descriptions"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/MIT-Missing-Semester/#resources","text":"Homepage: https://missing.csail.mit.edu/ Records: https://www.youtube.com/playlist?list=PLyzOVJj3bHQuloKGG59rS43e29ro7I57J Assignments: Some exercises after each lecture.","title":"Resources"},{"location":"en/%E7%BC%96%E8%AF%91%E5%8E%9F%E7%90%86/6035/","text":"","title":"6035"},{"location":"en/%E7%BC%96%E8%AF%91%E5%8E%9F%E7%90%86/CS143/","text":"Stanford CS143: Compilers \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784 \u7f16\u7a0b\u8bed\u8a00\uff1aJava \u6216 C++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 \u65af\u5766\u798f\u7684\u7f16\u8bd1\u539f\u7406\u8bfe\u7a0b\uff0c\u8bbe\u8ba1\u8005\u5f00\u53d1\u4e86\u4e00\u4e2a 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\u7406\u8bba\u90e8\u5206\u57fa\u672c\u6309\u7167\u9f99\u4e66\u7684\u987a\u5e8f\u8986\u76d6\u4e86\u8bcd\u6cd5\u5206\u6790\u3001\u8bed\u6cd5\u5206\u6790\u3001\u8bed\u4e49\u5206\u6790\u3001\u8fd0\u884c\u65f6\u73af\u5883\u3001\u5bc4\u5b58\u5668\u5206\u914d\u3001\u4ee3\u7801\u4f18\u5316\u4e0e\u751f\u6210\u7b49\u5185\u5bb9\uff0c\u5b9e\u8df5\u90e8\u5206\u5219\u76f8\u5e94\u5730\u5206\u4e3a\u8bcd\u6cd5\u5206\u6790\u3001\u8bed\u6cd5\u5206\u6790\u3001\u8bed\u4e49\u5206\u6790\u3001\u4ee3\u7801\u751f\u6210\u56db\u4e2a\u9636\u6bb5\uff0c\u96be\u5ea6\u5faa\u5e8f\u6e10\u8fdb\uff0c\u5e76\u5728\u4f18\u5316\u90e8\u5206\u7ed9\u5b66\u751f\u7559\u4e0b\u4e86\u5f88\u5927\u7684\u8bbe\u8ba1\u7a7a\u95f4\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs143/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV17K4y147Bz \u8bfe\u7a0b\u6559\u6750\uff1a\u9f99\u4e66 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a5 \u4e2a\u4e66\u9762\u4f5c\u4e1a + 5 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\u672c\u8bfe\u7a0b\u5c06\u4f5c\u4e3a\u57fa\u4e8e\u7269\u7406\u7684\u8ba1\u7b97\u673a\u52a8\u753b\u6280\u672f\u5165\u95e8\uff0c\u7740\u91cd\u4ecb\u7ecd\u5404\u79cd\u57fa\u7840\u7684\u7269\u7406\u52a8\u753b\u6a21\u62df\u6280\u672f\u3002 \u8be5\u8bfe\u7a0b\u4e3b\u8981\u6db5\u76d6\u56db\u4e2a\u65b9\u5411\uff0c\u5206\u522b\u4e3a\uff1a1\uff09\u521a\u4f53\u6a21\u62df\uff1b2\uff09\u8d28\u70b9\u5f39\u7c27\u3001\u7ea6\u675f\u4e0e\u5e03\u6599\u6a21\u62df\uff1b3\uff09\u57fa\u4e8e\u6709\u9650\u5143\u7684\u5f39\u6027\u4f53\u6a21\u62df\uff1b4\uff09\u6d41\u4f53\u6a21\u62df\u3002 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\u4e3b\u8981\u4ee5\u6e32\u67d3\u4e3a\u4e3b\uff0c\u800c\u5bf9\u4e8e\u7269\u7406\u6a21\u62df\uff0cGAMES103 \u5219\u662f\u5f88\u68d2\u7684\u5b66\u4e60\u8d44\u6e90\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E5%9B%BE%E5%BD%A2%E5%AD%A6/GAMES103/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a GAMES103 \u8bfe\u7a0b\u89c6\u9891\uff1a bilibili \u53c2\u8003\u8d44\u6599\uff1a \u8bfe\u7a0bPPT \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u5171\u56db\u6b21\u4f5c\u4e1a\uff0c\u53ef\u89c1\u5b98\u65b9BBS\u5c0f\u7a0b\u5e8f\u6216\u975e\u5b98\u65b9\u7684Repo\uff1a GAMES103 HW","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E5%9B%BE%E5%BD%A2%E5%AD%A6/GAMES103/#_3","text":"@indevn \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u8981\u6c42\u90fd\u6c47\u603b\u5728 GAMES103 Unoffical \u4e2d\u3002\u5bf9\u4e8e\u4f5c\u4e1a\u7684\u5177\u4f53\u5b9e\u73b0\uff0c\u5728\u77e5\u4e4e\u4e0a\u6709\u5f88\u591a\u76f8\u5173\u6587\u7ae0\u8fdb\u884c\u4e86\u7ec6\u81f4\u8bb2\u89e3\u53ef\u4ee5\u53c2\u8003\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E5%9B%BE%E5%BD%A2%E5%AD%A6/GAMES202/","text":"GAMES202 \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUCSB \u5148\u4fee\u8981\u6c42\uff1a\u7ebf\u6027\u4ee3\u6570\uff0c\u9ad8\u7b49\u6570\u5b66\uff0cC++\uff0cGAMES101 \u7f16\u7a0b\u8bed\u8a00\uff1aC++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a60 \u5c0f\u65f6 \u5b98\u65b9\u4ecb\u7ecd: \u672c\u8bfe\u7a0b\u5c06\u5168\u9762\u5730\u4ecb\u7ecd\u73b0\u4ee3\u5b9e\u65f6\u6e32\u67d3\u4e2d\u7684\u5173\u952e\u95ee\u9898\u4e0e\u89e3\u51b3\u65b9\u6cd5\u3002\u7531\u4e8e\u5b9e\u65f6\u6e32\u67d3 (>30 FPS) 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\u672c\u8bfe\u7a0b\u5c06\u4ee5\u4e13\u9898\u7684\u5f62\u5f0f\u5448\u73b0\uff0c\u8bfe\u7a0b\u5185\u5bb9\u4f1a\u8986\u76d6\u5b66\u672f\u754c\u4e0e\u5de5\u4e1a\u754c\u7684\u524d\u6cbf\u5185\u5bb9\uff0c\u5305\u62ec\uff1a\uff081\uff09\u5b9e\u65f6\u8f6f\u9634\u5f71\u7684\u6e32\u67d3\uff1b\uff082\uff09\u73af\u5883\u5149\u7167\uff1b\uff083\uff09\u57fa\u4e8e\u9884\u8ba1\u7b97\u6216\u65e0\u9884\u8ba1\u7b97\u7684\u5168\u5c40\u5149\u7167\uff1b\uff084\uff09\u57fa\u4e8e\u7269\u7406\u7684\u7740\u8272\u6a21\u578b\u4e0e\u65b9\u6cd5\uff1b\uff085\uff09\u5b9e\u65f6\u5149\u7ebf\u8ffd\u8e2a\uff1b\uff086\uff09\u6297\u952f\u9f7f\u4e0e\u8d85\u91c7\u6837\uff1b\u4ee5\u53ca\u4e00\u4e9b\u5e38\u89c1\u7684\u52a0\u901f\u65b9\u5f0f\u7b49\u7b49\u3002 \u9664\u4e86\u6700\u65b0\u6700\u5168\u7684\u5185\u5bb9\u4e4b\u5916\uff0c\u672c\u8bfe\u7a0b\u4e0e\u5176\u5b83\u4efb\u4f55\u5b9e\u65f6\u6e32\u67d3\u7684\u6559\u7a0b\u8fd8\u6709\u4e00\u4e2a\u91cd\u8981\u7684\u533a\u522b\uff0c\u90a3\u5c31\u662f\u672c\u8bfe\u7a0b\u4e0d\u4f1a\u8bb2\u6388\u4efb\u4f55\u4e0e\u6e38\u620f\u5f15\u64ce\u7684\u4f7f\u7528\u76f8\u5173\u7684\u5185\u5bb9\uff0c\u5e76\u4e14\u4e0d\u4f1a\u7279\u522b\u5f3a\u8c03\u5177\u4f53\u7684\u7740\u8272\u5668\u5b9e\u73b0\u6280\u672f\uff0c\u800c\u4e3b\u8981\u8bb2\u6388\u5b9e\u65f6\u6e32\u67d3\u80cc\u540e\u7684\u79d1\u5b66\u4e0e\u77e5\u8bc6\u3002\u672c\u8bfe\u7a0b\u7684\u76ee\u6807\u662f\u5728\u4f60\u5b66\u4e60\u5b8c\u8fd9\u95e8\u8bfe\u7684\u65f6\u5019\uff0c\u4f60\u5c06\u6709\u6df1\u539a\u7684\u529f\u5e95\u53bb\u5f00\u53d1\u4e00\u4e2a\u5c5e\u4e8e\u4f60\u81ea\u5df1\u7684\u5b9e\u65f6\u6e32\u67d3\u5f15\u64ce\u3002 \u4f5c\u4e3a GAMES101 \u7684\u8fdb\u9636\u8bfe\u7a0b\uff0c\u96be\u5ea6\u6709\u4e00\u5b9a\u7684\u63d0\u5347\uff0c\u4f46\u4e0d\u4f1a\u5f88\u5927\uff0c\u76f8\u4fe1\u5b8c\u6210\u4e86 GAMES101 \u7684\u540c\u5b66\u90fd\u6709\u80fd\u529b\u5b8c\u6210\u8fd9\u95e8\u8bfe\u7a0b\u3002\u6bcf\u4e2a project \u4ee3\u7801\u91cf\u90fd\u4e0d\u4f1a\u5f88\u591a\uff0c\u4f46\u662f\u90fd\u9700\u8981\u4e00\u5b9a\u7684\u601d\u8003\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a GAMES202 \u8bfe\u7a0b\u89c6\u9891\uff1a bilibili \u8bfe\u7a0b\u6559\u6750\uff1aReal-Time Rendering, 4th edition. \u8bfe\u7a0b\u4f5c\u4e1a\uff1a 5\u4e2aproject","title":"GAMES202"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E5%9B%BE%E5%BD%A2%E5%AD%A6/GAMES202/#games202","text":"","title":"GAMES202"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E5%9B%BE%E5%BD%A2%E5%AD%A6/GAMES202/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aUCSB \u5148\u4fee\u8981\u6c42\uff1a\u7ebf\u6027\u4ee3\u6570\uff0c\u9ad8\u7b49\u6570\u5b66\uff0cC++\uff0cGAMES101 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\u7684\u8fdb\u9636\u8bfe\u7a0b\uff0c\u96be\u5ea6\u6709\u4e00\u5b9a\u7684\u63d0\u5347\uff0c\u4f46\u4e0d\u4f1a\u5f88\u5927\uff0c\u76f8\u4fe1\u5b8c\u6210\u4e86 GAMES101 \u7684\u540c\u5b66\u90fd\u6709\u80fd\u529b\u5b8c\u6210\u8fd9\u95e8\u8bfe\u7a0b\u3002\u6bcf\u4e2a project \u4ee3\u7801\u91cf\u90fd\u4e0d\u4f1a\u5f88\u591a\uff0c\u4f46\u662f\u90fd\u9700\u8981\u4e00\u5b9a\u7684\u601d\u8003\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E5%9B%BE%E5%BD%A2%E5%AD%A6/GAMES202/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a GAMES202 \u8bfe\u7a0b\u89c6\u9891\uff1a bilibili \u8bfe\u7a0b\u6559\u6750\uff1aReal-Time Rendering, 4th edition. \u8bfe\u7a0b\u4f5c\u4e1a\uff1a 5\u4e2aproject","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/CS144/","text":"CS144: Computer Network Introduction Offered by: Stanford Prerequisites: Computer System Fundamentals, CS106L Programming Language: C++ Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours One of the lecturers of this course is Professor Nick McKeown , a giant in the field of Networking. At the end of each chapter of MOOC, he will interview an executive in the industry or an expert in the academia, which can certainly broaden your horizons. In the projects, you will use C++ to build the entire TCP/IP protocol stack, the IP router, and the ARP protocol step by step from scratch. Finally, you will replace Linux Kernel's protocol stack with your own and use socket programming to communicate with your classmates, which is really amazing and exciting. Resources Course Website: https://cs144.github.io/ Video: https://www.youtube.com/watch?v=r2WZNaFyrbQ&list=PL6RdenZrxrw9inR-IJv-erlOKRHjymxMN Textbook: None Assignments: refer to the course website Reference PKUFlyingPig Lexssama's Blogs huangrt01 kiprey \u5eb7\u5b87PL's Blog doraemonzzz ViXbob's libsponge \u5403\u7740\u571f\u8c46\u5750\u5730\u94c1\u7684\u535a\u5ba2 Smith \u661f\u9065\u89c1 EIMadrigal Joey","title":"Stanford CS144: Computer Network"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/CS144/#cs144-computer-network","text":"","title":"CS144: Computer Network"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/CS144/#introduction","text":"Offered by: Stanford Prerequisites: Computer System Fundamentals, CS106L Programming Language: C++ Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours One of the lecturers of this course is Professor Nick McKeown , a giant in the field of Networking. At the end of each chapter of MOOC, he will interview an executive in the industry or an expert in the academia, which can certainly broaden your horizons. In the projects, you will use C++ to build the entire TCP/IP protocol stack, the IP router, and the ARP protocol step by step from scratch. Finally, you will replace Linux Kernel's protocol stack with your own and use socket programming to communicate with your classmates, which is really amazing and exciting.","title":"Introduction"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/CS144/#resources","text":"Course Website: https://cs144.github.io/ Video: https://www.youtube.com/watch?v=r2WZNaFyrbQ&list=PL6RdenZrxrw9inR-IJv-erlOKRHjymxMN Textbook: None Assignments: refer to the course website","title":"Resources"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/CS144/#reference","text":"PKUFlyingPig Lexssama's Blogs huangrt01 kiprey \u5eb7\u5b87PL's Blog doraemonzzz ViXbob's libsponge \u5403\u7740\u571f\u8c46\u5750\u5730\u94c1\u7684\u535a\u5ba2 Smith \u661f\u9065\u89c1 EIMadrigal Joey","title":"Reference"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/topdown/","text":"Computer Networking: A Top-Down Approach Descriptions Offered by: UMass Prerequisites: basic knowledge about computer system Programming Languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 40 hours Computer Networking: A Top-Down Approach is a classic textbook in the field of computer networking. The two authors, Jim Kurose and Keith Ross, have carefully crafted a course website to support the textbook, with lecture recordings, interactive online questions, and WireShark labs for network packet analysis. The only pity is that this course doesn't have hardcore programming assignments, and Stanford's CS144 makes up for that. Course Resources Course Website: https://gaia.cs.umass.edu/kurose_ross/index.php Recordings: https://gaia.cs.umass.edu/kurose_ross/lectures.php Textbooks: Computer Networking: A Top-Down Approach Assignments: https://gaia.cs.umass.edu/kurose_ross/wireshark.php Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/Computer-Network-A-Top-Down-Approach - GitHub .","title":"Computer Networking: A Top-Down Approach"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/topdown/#computer-networking-a-top-down-approach","text":"","title":"Computer Networking: A Top-Down Approach"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/topdown/#descriptions","text":"Offered by: UMass Prerequisites: basic knowledge about computer system Programming Languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 40 hours Computer Networking: A Top-Down Approach is a classic textbook in the field of computer networking. The two authors, Jim Kurose and Keith Ross, have carefully crafted a course website to support the textbook, with lecture recordings, interactive online questions, and WireShark labs for network packet analysis. The only pity is that this course doesn't have hardcore programming assignments, and Stanford's CS144 makes up for that.","title":"Descriptions"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/topdown/#course-resources","text":"Course Website: https://gaia.cs.umass.edu/kurose_ross/index.php Recordings: https://gaia.cs.umass.edu/kurose_ross/lectures.php Textbooks: Computer Networking: A Top-Down Approach Assignments: https://gaia.cs.umass.edu/kurose_ross/wireshark.php","title":"Course Resources"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/topdown/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/Computer-Network-A-Top-Down-Approach - GitHub .","title":"Personal Resources"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/topdown_ustc/","text":"USTC Computer Networking:A Top-Down Approach \u8bfe\u7a0b\u7b80\u4ecb 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\u8bfe\u7a0b\u89c6\u9891\u662f\u90d1\u70c7\u8001\u5e08\u672c\u4eba\u5728\u54d4\u54e9\u54d4\u54e9\u4e0a\u4e0a\u4f20\u7684\uff0c\u4e14\u4ece2020\u5e74\u81f3\u4eca\u4e00\u76f4\u575a\u6301\u5728\u8bc4\u8bba\u533a\u7b54\u7591\uff0c\u5c5e\u5b9e\u8d1f\u8d23\u4e14\u8ba4\u771f\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/topdown_ustc/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://staff.ustc.edu.cn/~qzheng/teaching.html \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1JV411t7ow/ \u8bfe\u7a0b\u8bfe\u4ef6\uff1a http://staff.ustc.edu.cn/~qzheng/cn.zip \u8bfe\u7a0b\u6559\u6750\uff1a\u8ba1\u7b97\u673a\u7f51\u7edc\uff08\u81ea\u9876\u5411\u4e0b\u65b9\u6cd5 \u7b2c7\u7248\uff09\uff0c\u673a\u68b0\u5de5\u4e1a\u51fa\u7248\u793e\uff0c2016","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/6031/","text":"MIT 6.031: Software Construction \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u638c\u63e1\u81f3\u5c11\u4e00\u95e8\u7f16\u7a0b\u8bed\u8a00 \u7f16\u7a0b\u8bed\u8a00\uff1aJava \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u7684\u76ee\u6807\u5c31\u662f\u8ba9\u5b66\u751f\u5b66\u4f1a\u5982\u4f55\u5199\u51fa\u9ad8\u8d28\u91cf\u7684\u4ee3\u7801\uff0c\u6240\u8c13\u9ad8\u8d28\u91cf\uff0c\u5219\u662f\u6ee1\u8db3\u4e0b\u9762\u4e09\u4e2a\u76ee\u6807\uff08\u8bfe\u7a0b\u8bbe\u8ba1\u8005\u539f\u8bdd\u590d\u5236\uff0c\u4ee5\u9632\u81ea\u5df1\u7ffb\u8bd1\u66f2\u89e3\u672c\u610f\uff09\uff1a Safe from bugs. Correctness (correct behavior right now) and defensiveness (correct behavior in the future) are required in any software we build. Easy to understand. The code has to communicate to future programmers who need to understand it and make changes in it (fixing bugs or adding new features). That future programmer might be you, months or years from now. You\u2019ll be surprised how much you forget if you don\u2019t write it down, and how much it helps your own future self to have a good design. Ready for change. Software always changes. Some designs make it easy to make changes; others require throwing away and rewriting a lot of code. \u4e3a\u6b64\uff0c\u8fd9\u95e8\u8bfe\u7684\u8bbe\u8ba1\u8005\u4eec\u7cbe\u5fc3\u7f16\u5199\u4e86\u4e00\u672c\u4e66\u6765\u9610\u91ca\u8bf8\u591a\u8f6f\u4ef6\u6784\u5efa\u7684\u6838\u5fc3\u539f\u5219\u4e0e\u524d\u4eba\u603b\u7ed3\u4e0b\u6765\u7684\u5b9d\u8d35\u7ecf\u9a8c\uff0c\u5185\u5bb9\u7ec6\u8282\u5230\u5982\u4f55\u7f16\u5199\u6ce8\u91ca\u548c\u51fd\u6570 Specification\uff0c\u5982\u4f55\u8bbe\u8ba1\u62bd\u8c61\u6570\u636e\u7ed3\u6784\u4ee5\u53ca\u8bf8\u591a\u5e76\u884c\u7f16\u7a0b\u7684\u5185\u5bb9\uff0c\u5e76\u4e14\u4f1a\u8ba9\u4f60\u5728\u7cbe\u5fc3\u8bbe\u8ba1\u7684 Java \u7f16\u7a0b\u9879\u76ee\u91cc\u4f53\u9a8c\u548c\u7ec3\u4e60\u8fd9\u4e9b\u7f16\u7a0b\u6a21\u5f0f\u3002 2016\u5e74\u6625\u5b63\u5b66\u671f\u8fd9\u95e8\u8bfe\u5f00\u6e90\u4e86\u5176\u6240\u6709\u7f16\u7a0b\u4f5c\u4e1a\u7684\u4ee3\u7801\u6846\u67b6\uff0c\u800c\u6700\u65b0\u7684\u8bfe\u7a0b\u6559\u6750\u53ef\u4ee5\u5728\u5176\u6700\u65b0\u7684\u6559\u5b66\u7f51\u7ad9\u4e0a\u627e\u5230\uff0c\u5177\u4f53\u94fe\u63a5\u53c2\u89c1\u4e0b\u65b9\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a 2021spring , 2016spring \u8bfe\u7a0b\u89c6\u9891\uff1a\u65e0 \u8bfe\u7a0b\u6559\u6750\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9\u7684\u8bfe\u7a0b notes \u8bfe\u7a0b\u4f5c\u4e1a\uff1a4 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a + 1 \u4e2a Project \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/MIT6.031-software-construction - GitHub \u4e2d\u3002 @pengzhangzhi \u5b8c\u6210\u4e86\u8fd9\u95e8\u8bfe\u7684\u4f5c\u4e1a\u5e76\u8bb0\u5f55\u4e86\u7b14\u8bb0, \u4ee3\u7801\u5f00\u6e90\u5728 pengzhangzhi/self-taught-CS/Software Construction - Github \u3002","title":"MIT 6.031: Software Construction"},{"location":"en/%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/6031/#mit-6031-software-construction","text":"","title":"MIT 6.031: Software Construction"},{"location":"en/%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/6031/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u638c\u63e1\u81f3\u5c11\u4e00\u95e8\u7f16\u7a0b\u8bed\u8a00 \u7f16\u7a0b\u8bed\u8a00\uff1aJava \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u7684\u76ee\u6807\u5c31\u662f\u8ba9\u5b66\u751f\u5b66\u4f1a\u5982\u4f55\u5199\u51fa\u9ad8\u8d28\u91cf\u7684\u4ee3\u7801\uff0c\u6240\u8c13\u9ad8\u8d28\u91cf\uff0c\u5219\u662f\u6ee1\u8db3\u4e0b\u9762\u4e09\u4e2a\u76ee\u6807\uff08\u8bfe\u7a0b\u8bbe\u8ba1\u8005\u539f\u8bdd\u590d\u5236\uff0c\u4ee5\u9632\u81ea\u5df1\u7ffb\u8bd1\u66f2\u89e3\u672c\u610f\uff09\uff1a Safe from bugs. 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Some designs make it easy to make changes; others require throwing away and rewriting a lot of code. \u4e3a\u6b64\uff0c\u8fd9\u95e8\u8bfe\u7684\u8bbe\u8ba1\u8005\u4eec\u7cbe\u5fc3\u7f16\u5199\u4e86\u4e00\u672c\u4e66\u6765\u9610\u91ca\u8bf8\u591a\u8f6f\u4ef6\u6784\u5efa\u7684\u6838\u5fc3\u539f\u5219\u4e0e\u524d\u4eba\u603b\u7ed3\u4e0b\u6765\u7684\u5b9d\u8d35\u7ecf\u9a8c\uff0c\u5185\u5bb9\u7ec6\u8282\u5230\u5982\u4f55\u7f16\u5199\u6ce8\u91ca\u548c\u51fd\u6570 Specification\uff0c\u5982\u4f55\u8bbe\u8ba1\u62bd\u8c61\u6570\u636e\u7ed3\u6784\u4ee5\u53ca\u8bf8\u591a\u5e76\u884c\u7f16\u7a0b\u7684\u5185\u5bb9\uff0c\u5e76\u4e14\u4f1a\u8ba9\u4f60\u5728\u7cbe\u5fc3\u8bbe\u8ba1\u7684 Java \u7f16\u7a0b\u9879\u76ee\u91cc\u4f53\u9a8c\u548c\u7ec3\u4e60\u8fd9\u4e9b\u7f16\u7a0b\u6a21\u5f0f\u3002 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Pull Request\uff0c\u4e5f\u6b22\u8fce\u548c\u6211\u90ae\u4ef6\u8054\u7cfb\uff08 zhongyinmin@pku.edu.cn \uff09\u3002","title":"\u4f60\u4e5f\u60f3\u52a0\u5165\u5230\u8d21\u732e\u8005\u7684\u884c\u5217"},{"location":"#_8","text":"\u65b9\u6cd5\u53c2\u89c1\u4ed3\u5e93\u7684 README.md \u3002","title":"\u5173\u4e8e\u4ea4\u6d41\u7fa4\u7684\u5efa\u7acb"},{"location":"CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/","text":"\u4e00\u4e2a\u4ec5\u4f9b\u53c2\u8003\u7684 CS \u5b66\u4e60\u89c4\u5212 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Calculus Course \u548c 18.06: Linear Algebra \u7684\u8bfe\u7a0b notes\uff0c\u81f3\u5c11\u4e8e\u6211\u800c\u8a00\uff0c\u5b83\u5e2e\u52a9\u6211\u6df1\u523b\u7406\u89e3\u4e86\u5fae\u79ef\u5206\u548c\u7ebf\u6027\u4ee3\u6570\u7684\u8bb8\u591a\u672c\u8d28\u3002\u987a\u9053\u518d\u5b89\u5229\u4e00\u4e2a\u6cb9\u7ba1\u6570\u5b66\u7f51\u7ea2 3Blue1Brown \uff0c\u4ed6\u7684\u9891\u9053\u6709\u5f88\u591a\u7528\u751f\u52a8\u5f62\u8c61\u7684\u52a8\u753b\u9610\u91ca\u6570\u5b66\u672c\u8d28\u5185\u6838\u7684\u89c6\u9891\uff0c\u517c\u5177\u6df1\u5ea6\u548c\u5e7f\u5ea6\uff0c\u8d28\u91cf\u975e\u5e38\u9ad8\u3002 \u4fe1\u606f\u8bba\u5165\u95e8 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u53ca\u65e9\u4e86\u89e3\u4e00\u4e9b\u4fe1\u606f\u8bba\u7684\u57fa\u7840\u77e5\u8bc6\uff0c\u6211\u89c9\u5f97\u662f\u5927\u6709\u88e8\u76ca\u7684\u3002\u4f46\u5927\u591a\u4fe1\u606f\u8bba\u8bfe\u7a0b\u90fd\u9762\u5411\u9ad8\u5e74\u7ea7\u672c\u79d1\u751f\u751a\u81f3\u7814\u7a76\u751f\uff0c\u5bf9\u65b0\u624b\u6781\u4e0d\u53cb\u597d\u3002\u800c MIT \u7684 6.050J: Information theory and Entropy \u8fd9\u95e8\u8bfe\u6b63\u662f\u4e3a\u5927\u4e00\u65b0\u751f\u91cf\u8eab\u5b9a\u5236\u7684\uff0c\u51e0\u4e4e\u6ca1\u6709\u5148\u4fee\u8981\u6c42\uff0c\u6db5\u76d6\u4e86\u7f16\u7801\u3001\u538b\u7f29\u3001\u901a\u4fe1\u3001\u4fe1\u606f\u71b5\u7b49\u7b49\u5185\u5bb9\uff0c\u975e\u5e38\u6709\u8da3\u3002 \u6570\u5b66\u8fdb\u9636 \u79bb\u6563\u6570\u5b66\u4e0e\u6982\u7387\u8bba \u96c6\u5408\u8bba\u3001\u56fe\u8bba\u3001\u6982\u7387\u8bba\u7b49\u7b49\u662f\u7b97\u6cd5\u63a8\u5bfc\u4e0e\u8bc1\u660e\u7684\u91cd\u8981\u5de5\u5177\uff0c\u4e5f\u662f\u540e\u7eed\u9ad8\u9636\u6570\u5b66\u8bfe\u7a0b\u7684\u57fa\u7840\u3002\u4f46\u6211\u89c9\u5f97\u8fd9\u7c7b\u8bfe\u7a0b\u7684\u8bb2\u6388\u5f88\u5bb9\u6613\u843d\u5165\u7406\u8bba\u5316\u4e0e\u5f62\u5f0f\u5316\u7684\u7aa0\u81fc\uff0c\u8ba9\u8bfe\u5802\u6210\u4e3a\u5b9a\u7406\u7ed3\u8bba\u7684\u5806\u780c\uff0c\u800c\u65e0\u6cd5\u4f7f\u5b66\u751f\u6df1\u523b\u628a\u63e1\u7406\u8bba\u7684\u672c\u8d28\uff0c\u8fdb\u800c\u9020\u6210\u5b66\u4e86\u5c31\u80cc\uff0c\u8003\u4e86\u5c31\u5fd8\u7684\u602a\u5708\u3002\u5982\u679c\u80fd\u5728\u7406\u8bba\u6559\u5b66\u4e2d\u7a7f\u63d2\u7b97\u6cd5\u8fd0\u7528\u5b9e\u4f8b\uff0c\u5b66\u751f\u5728\u62d3\u5c55\u7b97\u6cd5\u77e5\u8bc6\u7684\u540c\u65f6\u4e5f\u80fd\u7aa5\u89c1\u7406\u8bba\u7684\u529b\u91cf\u548c\u9b45\u529b\u3002 UCB CS70 : discrete Math and probability theory \u548c UCB CS126 : Probability theory \u662f UC Berkeley \u7684\u6982\u7387\u8bba\u8bfe\u7a0b\uff0c\u524d\u8005\u8986\u76d6\u4e86\u79bb\u6563\u6570\u5b66\u548c\u6982\u7387\u8bba\u57fa\u7840\uff0c\u540e\u8005\u5219\u6d89\u53ca\u968f\u673a\u8fc7\u7a0b\u4ee5\u53ca\u6df1\u5165\u7684\u7406\u8bba\u5185\u5bb9\u3002\u4e24\u8005\u90fd\u975e\u5e38\u6ce8\u91cd\u7406\u8bba\u548c\u5b9e\u8df5\u7684\u7ed3\u5408\uff0c\u6709\u4e30\u5bcc\u7684\u7b97\u6cd5\u5b9e\u9645\u8fd0\u7528\u5b9e\u4f8b\uff0c\u540e\u8005\u8fd8\u6709\u5927\u91cf\u7684 Python \u7f16\u7a0b\u4f5c\u4e1a\u6765\u8ba9\u5b66\u751f\u8fd0\u7528\u6982\u7387\u8bba\u7684\u77e5\u8bc6\u89e3\u51b3\u5b9e\u9645\u95ee\u9898\u3002 \u6570\u503c\u5206\u6790 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u57f9\u517b\u8ba1\u7b97\u601d\u7ef4\u662f\u5f88\u91cd\u8981\u7684\uff0c\u5b9e\u9645\u95ee\u9898\u7684\u5efa\u6a21\u3001\u79bb\u6563\u5316\uff0c\u8ba1\u7b97\u673a\u7684\u6a21\u62df\u3001\u5206\u6790\uff0c\u662f\u4e00\u9879\u5f88\u91cd\u8981\u7684\u80fd\u529b\u3002\u800c\u8fd9\u4e24\u5e74\u5f00\u59cb\u98ce\u9761\u7684\uff0c\u7531 MIT \u6253\u9020\u7684 Julia \u7f16\u7a0b\u8bed\u8a00\u4ee5\u5176 C \u4e00\u6837\u7684\u901f\u5ea6\u548c Python \u4e00\u6837\u53cb\u597d\u7684\u8bed\u6cd5\u5728\u6570\u503c\u8ba1\u7b97\u9886\u57df\u6709\u4e00\u7edf\u5929\u4e0b\u4e4b\u52bf\uff0cMIT \u7684\u8bb8\u591a\u6570\u5b66\u8bfe\u7a0b\u4e5f\u5f00\u59cb\u7528 Julia \u4f5c\u4e3a\u6559\u5b66\u5de5\u5177\uff0c\u628a\u8270\u6df1\u7684\u6570\u5b66\u7406\u8bba\u7528\u76f4\u89c2\u6e05\u6670\u7684\u4ee3\u7801\u5c55\u793a\u51fa\u6765\u3002 ComputationalThinking \u662f MIT \u5f00\u8bbe\u7684\u4e00\u95e8\u8ba1\u7b97\u601d\u7ef4\u5165\u95e8\u8bfe\uff0c\u6240\u6709\u8bfe\u7a0b\u5185\u5bb9\u5168\u90e8\u5f00\u6e90\uff0c\u53ef\u4ee5\u5728\u8bfe\u7a0b\u7f51\u7ad9\u76f4\u63a5\u8bbf\u95ee\u3002\u8fd9\u95e8\u8bfe\u5229\u7528 Julia \u7f16\u7a0b\u8bed\u8a00\uff0c\u5728\u56fe\u50cf\u5904\u7406\u3001\u793e\u4f1a\u79d1\u5b66\u4e0e\u6570\u636e\u79d1\u5b66\u3001\u6c14\u5019\u5b66\u5efa\u6a21\u4e09\u4e2a topic \u4e0b\u5e26\u9886\u5b66\u751f\u7406\u89e3\u7b97\u6cd5\u3001\u6570\u5b66\u5efa\u6a21\u3001\u6570\u636e\u5206\u6790\u3001\u4ea4\u4e92\u8bbe\u8ba1\u3001\u56fe\u4f8b\u5c55\u793a\uff0c\u8ba9\u5b66\u751f\u4f53\u9a8c\u8ba1\u7b97\u4e0e\u79d1\u5b66\u7684\u7f8e\u5999\u7ed3\u5408\u3002\u5185\u5bb9\u867d\u7136\u4e0d\u96be\uff0c\u4f46\u7ed9\u6211\u6700\u6df1\u523b\u7684\u611f\u53d7\u5c31\u662f\uff0c\u79d1\u5b66\u7684\u9b45\u529b\u5e76\u4e0d\u662f\u6545\u5f04\u7384\u865a\u7684\u8270\u6df1\u7406\u8bba\uff0c\u4e0d\u662f\u8bd8\u5c48\u8071\u7259\u7684\u672f\u8bed\u884c\u8bdd\uff0c\u800c\u662f\u7528\u76f4\u89c2\u751f\u52a8\u7684\u6848\u4f8b\uff0c\u7528\u7b80\u7ec3\u6df1\u523b\u7684\u8bed\u8a00\uff0c\u8ba9\u6bcf\u4e2a\u666e\u901a\u4eba\u90fd\u80fd\u7406\u89e3\u3002 \u4e0a\u5b8c\u4e0a\u9762\u7684\u4f53\u9a8c\u8bfe\u4e4b\u540e\uff0c\u5982\u679c\u610f\u72b9\u672a\u5c3d\u7684\u8bdd\uff0c\u4e0d\u59a8\u8bd5\u8bd5 MIT \u7684 18.330 : Introduction to numerical analysis \uff0c\u8fd9\u95e8\u8bfe\u7684\u7f16\u7a0b\u4f5c\u4e1a\u540c\u6837\u4f1a\u7528 Julia \u7f16\u7a0b\u8bed\u8a00\uff0c\u4e0d\u8fc7\u96be\u5ea6\u548c\u6df1\u5ea6\u4e0a\u90fd\u4e0a\u4e86\u4e00\u4e2a\u53f0\u9636\u3002\u5185\u5bb9\u6d89\u53ca\u4e86\u6d6e\u70b9\u7f16\u7801\u3001Root finding\u3001\u7ebf\u6027\u7cfb\u7edf\u3001\u5fae\u5206\u65b9\u7a0b\u7b49\u7b49\u65b9\u9762\uff0c\u6574\u95e8\u8bfe\u7684\u4e3b\u65e8\u5c31\u662f\u8ba9\u4f60\u5229\u7528\u79bb\u6563\u5316\u7684\u8ba1\u7b97\u673a\u8868\u793a\u53bb\u4f30\u8ba1\u548c\u903c\u8fd1\u4e00\u4e2a\u6570\u5b66\u4e0a\u8fde\u7eed\u7684\u6982\u5ff5\u3002\u8fd9\u95e8\u8bfe\u7684\u6559\u6388\u8fd8\u4e13\u95e8\u64b0\u5199\u4e86\u4e00\u672c\u914d\u5957\u7684\u5f00\u6e90\u6559\u6750 Fundamentals of Numerical Computation \uff0c\u91cc\u9762\u9644\u6709\u4e30\u5bcc\u7684 Julia \u4ee3\u7801\u5b9e\u4f8b\u548c\u4e25\u8c28\u7684\u516c\u5f0f\u63a8\u5bfc\u3002 \u5982\u679c\u4f60\u8fd8\u610f\u72b9\u672a\u5c3d\u7684\u8bdd\uff0c\u8fd8\u6709 MIT \u7684\u6570\u503c\u5206\u6790\u7814\u7a76\u751f\u8bfe\u7a0b 18.335: Introduction to numerical method \u4f9b\u4f60\u53c2\u8003\u3002 \u5fae\u5206\u65b9\u7a0b \u5982\u679c\u4e16\u95f4\u4e07\u7269\u7684\u8fd0\u52a8\u53d1\u5c55\u90fd\u80fd\u7528\u65b9\u7a0b\u6765\u523b\u753b\u548c\u63cf\u8ff0\uff0c\u8fd9\u662f\u4e00\u4ef6\u591a\u4e48\u9177\u7684\u4e8b\u60c5\u5440\uff01\u867d\u7136\u51e0\u4e4e\u4efb\u4f55\u4e00\u6240\u5b66\u6821\u7684 CS \u57f9\u517b\u65b9\u6848\u4e2d\u90fd\u6ca1\u6709\u5fae\u5206\u65b9\u7a0b\u76f8\u5173\u7684\u5fc5\u4fee\u8bfe\u7a0b\uff0c\u4f46\u6211\u8fd8\u662f\u89c9\u5f97\u638c\u63e1\u5b83\u4f1a\u8d4b\u4e88\u4f60\u4e00\u4e2a\u65b0\u7684\u89c6\u89d2\u6765\u5ba1\u89c6\u8fd9\u4e2a\u4e16\u754c\u3002 \u7531\u4e8e\u5fae\u5206\u65b9\u7a0b\u4e2d\u5f80\u5f80\u4f1a\u7528\u5230\u5f88\u591a\u590d\u53d8\u51fd\u6570\u7684\u77e5\u8bc6\uff0c\u6240\u4ee5\u5927\u5bb6\u53ef\u4ee5\u53c2\u8003 MIT18.04: Complex variables functions \u7684\u8bfe\u7a0b notes \u6765\u8865\u9f50\u5148\u4fee\u77e5\u8bc6\u3002 MIT18.03: differential equations ) \u4e3b\u8981\u8986\u76d6\u4e86\u5e38\u5fae\u5206\u65b9\u7a0b\u7684\u6c42\u89e3\uff0c\u5728\u6b64\u57fa\u7840\u4e4b\u4e0a MIT18.152: Partial differential equations ) \u5219\u4f1a\u6df1\u5165\u504f\u5fae\u5206\u65b9\u7a0b\u7684\u5efa\u6a21\u4e0e\u6c42\u89e3\u3002\u638c\u63e1\u4e86\u5fae\u5206\u65b9\u7a0b\u8fd9\u4e00\u6709\u5229\u5de5\u5177\uff0c\u76f8\u4fe1\u5bf9\u4e8e\u4f60\u7684\u5b9e\u9645\u95ee\u9898\u7684\u5efa\u6a21\u80fd\u529b\u4ee5\u53ca\u4ece\u4f17\u591a\u566a\u58f0\u53d8\u91cf\u4e2d\u628a\u63e1\u672c\u8d28\u7684\u76f4\u89c9\u90fd\u4f1a\u6709\u5f88\u5927\u5e2e\u52a9\u3002 \u6570\u5b66\u9ad8\u9636 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u6211\u7ecf\u5e38\u542c\u5230\u6570\u5b66\u65e0\u7528\u8bba\u7684\u8bba\u65ad\uff0c\u5bf9\u6b64\u6211\u4e0d\u6562\u82df\u540c\u4f46\u4e5f\u65e0\u6743\u53cd\u5bf9\uff0c\u4f46\u82e5\u51e1\u4e8b\u90fd\u786c\u8981\u4e89\u51fa\u4e2a\u6709\u7528\u548c\u65e0\u7528\u7684\u533a\u522b\u6765\uff0c\u5012\u4e5f\u7740\u5b9e\u65e0\u8da3\uff0c\u56e0\u6b64\u4e0b\u9762\u8fd9\u4e9b\u9762\u5411\u9ad8\u5e74\u7ea7\u751a\u81f3\u7814\u7a76\u751f\u7684\u6570\u5b66\u8bfe\u7a0b\uff0c\u5927\u5bb6\u6309\u5174\u8da3\u81ea\u53d6\u6240\u9700\u3002 \u51f8\u4f18\u5316 Standford EE364A: Convex Optimization \u4fe1\u606f\u8bba MIT6.441: Information Theory \u5e94\u7528\u7edf\u8ba1\u5b66 MIT18.650: Statistics for Applications \u521d\u7b49\u6570\u8bba MIT18.781: Theory of Numbers \u5bc6\u7801\u5b66 Standford CS255: Cryptography \u7f16\u7a0b\u5165\u95e8 Languages are tools, you choose the right tool to do the right thing. Since there's no universally perfect tool, there's no universally perfect language. Shell MIT-Missing-Semester Python Harvard CS50: This is CS50x UCB CS61A: Structure and Interpretation of Computer Programs C++ Stanford CS106B/X: Programming Abstractions Stanford CS106L: Standard C++ Programming Rust Stanford CS110L: Safety in Systems Programming OCaml Cornell CS3110 textbook: Functional Programming in OCaml \u7535\u5b50\u57fa\u7840 \u7535\u8def\u57fa\u7840 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u4e86\u89e3\u4e00\u4e9b\u57fa\u7840\u7684\u7535\u8def\u77e5\u8bc6\uff0c\u611f\u53d7\u4ece\u4f20\u611f\u5668\u6536\u96c6\u6570\u636e\u5230\u6570\u636e\u5206\u6790\u518d\u5230\u7b97\u6cd5\u9884\u6d4b\u6574\u6761\u6d41\u6c34\u7ebf\uff0c\u5bf9\u4e8e\u540e\u7eed\u77e5\u8bc6\u7684\u5b66\u4e60\u4ee5\u53ca\u8ba1\u7b97\u601d\u7ef4\u7684\u57f9\u517b\u8fd8\u662f\u5f88\u6709\u5e2e\u52a9\u7684\u3002 EE16A&B: Designing Information Devices and Systems I&II \u662f\u4f2f\u514b\u5229 EE \u5b66\u751f\u7684\u5927\u4e00\u5165\u95e8\u8bfe\uff0c\u5176\u4e2d EE16A \u6ce8\u91cd\u901a\u8fc7\u7535\u8def\u4ece\u5b9e\u9645\u73af\u5883\u4e2d\u6536\u96c6\u548c\u5206\u6790\u6570\u636e\uff0c\u800c EE16B \u5219\u4fa7\u91cd\u4ece\u8fd9\u4e9b\u6536\u96c6\u5230\u7684\u6570\u636e\u8fdb\u884c\u5206\u6790\u5e76\u505a\u51fa\u9884\u6d4b\u884c\u4e3a\u3002 \u4fe1\u53f7\u4e0e\u7cfb\u7edf \u4fe1\u53f7\u4e0e\u7cfb\u7edf\u662f\u4e00\u95e8\u6211\u89c9\u5f97\u975e\u5e38\u503c\u5f97\u4e00\u4e0a\u7684\u8bfe\uff0c\u6700\u521d\u5b66\u5b83\u53ea\u662f\u4e3a\u4e86\u6ee1\u8db3\u6211\u5bf9\u5085\u91cc\u53f6\u53d8\u6362\u7684\u597d\u5947\uff0c\u4f46\u5b66\u5b8c\u4e4b\u540e\u6211\u624d\u4e0d\u7981\u611f\u53f9\uff0c\u5085\u7acb\u53f6\u53d8\u6362\u7ed9\u6211\u63d0\u4f9b\u4e86\u4e00\u4e2a\u5168\u65b0\u7684\u89c6\u89d2\u53bb\u770b\u5f85\u8fd9\u4e2a\u4e16\u754c\uff0c\u5c31\u5982\u540c\u5fae\u5206\u65b9\u7a0b\u4e00\u6837\uff0c\u8ba9\u4f60\u6c89\u6d78\u5728\u7528\u6570\u5b66\u53bb\u7cbe\u786e\u63cf\u7ed8\u548c\u523b\u753b\u8fd9\u4e2a\u4e16\u754c\u7684\u4f18\u96c5\u4e0e\u795e\u5947\u4e4b\u4e2d\u3002 MIT 6.003: signal and systems \u63d0\u4f9b\u4e86\u5168\u90e8\u7684\u8bfe\u7a0b\u5f55\u5f71\u3001\u4e66\u9762\u4f5c\u4e1a\u4ee5\u53ca\u7b54\u6848\u3002\u4e5f\u53ef\u4ee5\u53bb\u770b\u8fd9\u95e8\u8bfe\u7684 \u8fdc\u53e4\u7248\u672c \u800c UCB EE120: Signal and Systems \u5173\u4e8e\u5085\u7acb\u53f6\u53d8\u6362\u7684 notes \u5199\u5f97\u975e\u5e38\u597d\uff0c\u5e76\u4e14\u63d0\u4f9b\u4e866 \u4e2a\u975e\u5e38\u6709\u8da3\u7684 Python \u7f16\u7a0b\u4f5c\u4e1a\uff0c\u8ba9\u4f60\u5b9e\u8df5\u4e2d\u8fd0\u7528\u4fe1\u53f7\u4e0e\u7cfb\u7edf\u7684\u7406\u8bba\u4e0e\u7b97\u6cd5\u3002 \u6570\u636e\u7ed3\u6784\u4e0e\u7b97\u6cd5 \u6570\u636e\u7ed3\u6784\u4e0e\u7b97\u6cd5 UCB CS61B: Data Structures and Algorithms Coursera: Algorithms I & II \u7b97\u6cd5\u8bbe\u8ba1\u4e0e\u5206\u6790 UCB CS170: Efficient Algorithms and Intractable Problems \u8f6f\u4ef6\u5de5\u7a0b \u5165\u95e8\u8bfe \u4e00\u4efd\u201c\u80fd\u8dd1\u201d\u7684\u4ee3\u7801\uff0c\u548c\u4e00\u4efd\u9ad8\u8d28\u91cf\u7684\u5de5\u4e1a\u7ea7\u4ee3\u7801\u662f\u6709\u672c\u8d28\u533a\u522b\u7684\u3002\u56e0\u6b64\u6211\u975e\u5e38\u63a8\u8350\u4f4e\u5e74\u7ea7\u7684\u540c\u5b66\u5b66\u4e60\u4e00\u4e0b MIT 6.031: Software Construction \u8fd9\u95e8\u8bfe\uff0c\u5b83\u4f1a\u4ee5 Java \u8bed\u8a00\u4e3a\u57fa\u7840\uff0c\u4ee5\u4e30\u5bcc\u7ec6\u81f4\u7684\u9605\u8bfb\u6750\u6599\u548c\u7cbe\u5fc3\u8bbe\u8ba1\u7684\u7f16\u7a0b\u7ec3\u4e60\u4f20\u6388\u5982\u4f55\u7f16\u5199 \u4e0d\u6613\u51fa bug\u3001\u7b80\u660e\u6613\u61c2\u3001\u6613\u4e8e\u7ef4\u62a4\u4fee\u6539 \u7684\u9ad8\u8d28\u91cf\u4ee3\u7801\u3002\u5927\u5230\u5b8f\u89c2\u6570\u636e\u7ed3\u6784\u8bbe\u8ba1\uff0c\u5c0f\u5230\u5982\u4f55\u5199\u6ce8\u91ca\uff0c\u9075\u5faa\u8fd9\u4e9b\u524d\u4eba\u603b\u7ed3\u7684\u7ec6\u8282\u548c\u7ecf\u9a8c\uff0c\u5bf9\u4e8e\u4f60\u6b64\u540e\u7684\u7f16\u7a0b\u751f\u6daf\u5927\u6709\u88e8\u76ca\u3002 \u4e13\u4e1a\u8bfe \u5f53\u7136\uff0c\u5982\u679c\u4f60\u60f3\u7cfb\u7edf\u6027\u5730\u4e0a\u4e00\u95e8\u8f6f\u4ef6\u5de5\u7a0b\u7684\u8bfe\u7a0b\uff0c\u90a3\u6211\u63a8\u8350\u7684\u662f\u4f2f\u514b\u5229\u7684 UCB CS169: software engineering \u3002\u4f46\u9700\u8981\u63d0\u9192\u7684\u662f\uff0c\u548c\u5927\u591a\u5b66\u6821\uff08\u5305\u62ec\u8d35\u6821\uff09\u7684\u8f6f\u4ef6\u5de5\u7a0b\u8bfe\u7a0b\u4e0d\u540c\uff0c\u8fd9\u95e8\u8bfe\u4e0d\u4f1a\u6d89\u53ca\u4f20\u7edf\u7684 design and document \u6a21\u5f0f\uff0c\u5373\u5f3a\u8c03\u5404\u79cd\u7c7b\u56fe\u3001\u6d41\u7a0b\u56fe\u53ca\u6587\u6863\u8bbe\u8ba1\uff0c\u800c\u662f\u91c7\u7528\u8fd1\u4e9b\u5e74\u6d41\u884c\u8d77\u6765\u7684\u5c0f\u56e2\u961f\u5feb\u901f\u8fed\u4ee3 Agile Develepment \u5f00\u53d1\u6a21\u5f0f\u4ee5\u53ca\u5229\u7528\u4e91\u5e73\u53f0\u7684 Software as a service \u670d\u52a1\u6a21\u5f0f\u3002 \u4f53\u7cfb\u7ed3\u6784 \u5165\u95e8\u8bfe \u4ece\u5c0f\u6211\u5c31\u4e00\u76f4\u542c\u8bf4\uff0c\u8ba1\u7b97\u673a\u7684\u4e16\u754c\u662f\u7531 01 \u6784\u6210\u7684\uff0c\u6211\u4e0d\u7406\u89e3\u4f46\u5927\u53d7\u9707\u64bc\u3002\u5982\u679c\u4f60\u7684\u5185\u5fc3\u4e5f\u6000\u6709\u8fd9\u4efd\u597d\u5947\uff0c\u4e0d\u59a8\u82b1\u4e00\u5230\u4e24\u4e2a\u6708\u7684\u65f6\u95f4\u5b66\u4e60 Coursera: Nand2Tetris \u8fd9\u95e8\u65e0\u95e8\u69db\u7684\u8ba1\u7b97\u673a\u8bfe\u7a0b\u3002\u8fd9\u95e8\u9ebb\u96c0\u867d\u5c0f\u4e94\u810f\u4ff1\u5168\u7684\u8bfe\u7a0b\u4f1a\u4ece 01 \u5f00\u59cb\u8ba9\u4f60\u4eb2\u624b\u9020\u51fa\u4e00\u53f0\u8ba1\u7b97\u673a\uff0c\u5e76\u5728\u4e0a\u9762\u8fd0\u884c\u4fc4\u7f57\u65af\u65b9\u5757\u5c0f\u6e38\u620f\u3002\u4e00\u95e8\u8bfe\u91cc\u6db5\u76d6\u4e86\u7f16\u8bd1\u3001\u865a\u62df\u673a\u3001\u6c47\u7f16\u3001\u4f53\u7cfb\u7ed3\u6784\u3001\u6570\u5b57\u7535\u8def\u3001\u903b\u8f91\u95e8\u7b49\u7b49\u4ece\u4e0a\u81f3\u4e0b\u3001\u4ece\u8f6f\u81f3\u786c\u7684\u5404\u7c7b\u77e5\u8bc6\uff0c\u975e\u5e38\u5168\u9762\u3002\u96be\u5ea6\u4e0a\u4e5f\u662f\u901a\u8fc7\u7cbe\u5fc3\u7684\u8bbe\u8ba1\uff0c\u7565\u53bb\u4e86\u4f17\u591a\u73b0\u4ee3\u8ba1\u7b97\u673a\u590d\u6742\u7684\u7ec6\u8282\uff0c\u63d0\u53d6\u51fa\u4e86\u6700\u6838\u5fc3\u672c\u8d28\u7684\u4e1c\u897f\uff0c\u529b\u56fe\u8ba9\u6bcf\u4e2a\u4eba\u90fd\u80fd\u7406\u89e3\u3002\u5728\u4f4e\u5e74\u7ea7\uff0c\u5982\u679c\u5c31\u80fd\u4ece\u5b8f\u89c2\u4e0a\u5efa\u7acb\u5bf9\u6574\u4e2a\u8ba1\u7b97\u673a\u4f53\u7cfb\u7684\u9e1f\u77b0\u56fe\uff0c\u662f\u5927\u6709\u88e8\u76ca\u7684\u3002 \u4e13\u4e1a\u8bfe \u5f53\u7136\uff0c\u5982\u679c\u60f3\u6df1\u5165\u73b0\u4ee3\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\u7684\u590d\u6742\u7ec6\u8282\uff0c\u8fd8\u5f97\u4e0a\u4e00\u95e8\u5927\u5b66\u672c\u79d1\u96be\u5ea6\u7684\u8bfe\u7a0b UCB CS61C: Great Ideas in Computer Architecture \u3002UC Berkeley \u4f5c\u4e3a RISC-V \u67b6\u6784\u7684\u53d1\u6e90\u5730\uff0c\u5728\u4f53\u7cfb\u7ed3\u6784\u9886\u57df\u7b97\u5f97\u4e0a\u9996\u5c48\u4e00\u6307\u3002\u5176\u8bfe\u7a0b\u975e\u5e38\u6ce8\u91cd\u5b9e\u8df5\uff0c\u4f60\u4f1a\u5728 Project \u4e2d\u624b\u5199\u6c47\u7f16\u6784\u9020\u795e\u7ecf\u7f51\u7edc\uff0c\u4ece\u96f6\u5f00\u59cb\u642d\u5efa\u4e00\u4e2a CPU\uff0c\u8fd9\u4e9b\u5b9e\u8df5\u90fd\u4f1a\u8ba9\u4f60\u5bf9\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\u6709\u66f4\u4e3a\u6df1\u5165\u7684\u7406\u89e3\uff0c\u800c\u4e0d\u662f\u4ec5\u505c\u7559\u4e8e\u201c\u53d6\u6307\u8bd1\u7801\u6267\u884c\u8bbf\u5b58\u5199\u56de\u201d\u7684\u5355\u8c03\u80cc\u8bf5\u91cc\u3002 \u7cfb\u7edf\u5165\u95e8 \u8ba1\u7b97\u673a\u7cfb\u7edf\u662f\u4e00\u4e2a\u5e9e\u6742\u800c\u6df1\u523b\u7684\u4e3b\u9898\uff0c\u5728\u6df1\u5165\u5b66\u4e60\u67d0\u4e2a\u7ec6\u5206\u9886\u57df\u4e4b\u524d\uff0c\u5bf9\u5404\u4e2a\u9886\u57df\u6709\u4e00\u4e2a\u5b8f\u89c2\u6982\u5ff5\u6027\u7684\u7406\u89e3\uff0c\u5bf9\u4e00\u4e9b\u901a\u7528\u6027\u7684\u8bbe\u8ba1\u539f\u5219\u6709\u6240\u77e5\u6653\uff0c\u4f1a\u8ba9\u4f60\u5728\u4e4b\u540e\u7684\u6df1\u5165\u5b66\u4e60\u4e2d\u4e0d\u65ad\u5f3a\u5316\u4e00\u4e9b\u6700\u4e3a\u6838\u5fc3\u4e43\u81f3\u54f2\u5b66\u7684\u6982\u5ff5\uff0c\u800c\u4e0d\u4f1a\u684e\u688f\u4e8e\u590d\u6742\u7684\u5185\u90e8\u7ec6\u8282\u548c\u5404\u79cd trick\u3002\u56e0\u4e3a\u5728\u6211\u770b\u6765\uff0c\u5b66\u4e60\u7cfb\u7edf\u6700\u5173\u952e\u7684\u8fd8\u662f\u60f3\u8ba9\u4f60\u9886\u609f\u5230\u8fd9\u4e9b\u6700\u6838\u5fc3\u7684\u4e1c\u897f\uff0c\u4ece\u800c\u80fd\u591f\u8bbe\u8ba1\u548c\u5b9e\u73b0\u51fa\u5c5e\u4e8e\u81ea\u5df1\u7684\u7cfb\u7edf\u3002 MIT6.033: System Engineering \u662f MIT \u7684\u7cfb\u7edf\u5165\u95e8\u8bfe\uff0c\u4e3b\u9898\u6d89\u53ca\u4e86\u64cd\u4f5c\u7cfb\u7edf\u3001\u7f51\u7edc\u3001\u5206\u5e03\u5f0f\u548c\u7cfb\u7edf\u5b89\u5168\uff0c\u9664\u4e86\u77e5\u8bc6\u70b9\u7684\u4f20\u6388\u5916\uff0c\u8fd9\u95e8\u8bfe\u8fd8\u4f1a\u8bb2\u6388\u4e00\u4e9b\u5199\u4f5c\u548c\u8868\u8fbe\u4e0a\u7684\u6280\u5de7\uff0c\u8ba9\u4f60\u5b66\u4f1a\u5982\u4f55\u8bbe\u8ba1\u5e76\u5411\u522b\u4eba\u4ecb\u7ecd\u548c\u5206\u6790\u81ea\u5df1\u7684\u7cfb\u7edf\u3002\u8fd9\u672c\u4e66\u914d\u5957\u7684\u6559\u6750 Principles of Computer System Design: An Introduction \u4e5f\u5199\u5f97\u975e\u5e38\u597d\uff0c\u63a8\u8350\u5927\u5bb6\u9605\u8bfb\u3002 CMU 15-213: Introduction to Computer System \u662f CMU 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OCaml","title":"OCaml"},{"location":"CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_20","text":"","title":"\u7535\u5b50\u57fa\u7840"},{"location":"CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_21","text":"\u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u4e86\u89e3\u4e00\u4e9b\u57fa\u7840\u7684\u7535\u8def\u77e5\u8bc6\uff0c\u611f\u53d7\u4ece\u4f20\u611f\u5668\u6536\u96c6\u6570\u636e\u5230\u6570\u636e\u5206\u6790\u518d\u5230\u7b97\u6cd5\u9884\u6d4b\u6574\u6761\u6d41\u6c34\u7ebf\uff0c\u5bf9\u4e8e\u540e\u7eed\u77e5\u8bc6\u7684\u5b66\u4e60\u4ee5\u53ca\u8ba1\u7b97\u601d\u7ef4\u7684\u57f9\u517b\u8fd8\u662f\u5f88\u6709\u5e2e\u52a9\u7684\u3002 EE16A&B: Designing Information Devices and Systems I&II \u662f\u4f2f\u514b\u5229 EE \u5b66\u751f\u7684\u5927\u4e00\u5165\u95e8\u8bfe\uff0c\u5176\u4e2d EE16A \u6ce8\u91cd\u901a\u8fc7\u7535\u8def\u4ece\u5b9e\u9645\u73af\u5883\u4e2d\u6536\u96c6\u548c\u5206\u6790\u6570\u636e\uff0c\u800c EE16B 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construction.","title":"\u57f9\u517b\u65b9\u6848Pro"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/","text":"\u597d\u4e66\u63a8\u8350 \u7531\u4e8e\u7248\u6743\u539f\u56e0\uff0c\u4e0b\u9762\u5217\u4e3e\u7684\u56fe\u4e66\u4e2d\u9664\u4e86\u5f00\u6e90\u8d44\u6e90\u63d0\u4f9b\u4e86\u94fe\u63a5\uff0c\u5176\u4ed6\u7684\u8d44\u6e90\u8bf7\u5927\u5bb6\u81ea\u884c\u901a\u8fc7 libgen \u6216 z-lib \u67e5\u627e\u3002 \u8d44\u6e90\u6c47\u603b Free Programming Books : \u5f00\u6e90\u7f16\u7a0b\u4e66\u7c4d\u8d44\u6e90\u6c47\u603b CS Textbook Recommendations : \u8ba1\u7b97\u673a\u79d1\u5b66\u65b9\u5411\u63a8\u8350\u6559\u6750\u5217\u8868 C Book Guide and List : C\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 C++ Book Guide and List : C++\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 Python Book Guide and List : Python\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 \u7cfb\u7edf\u5165\u95e8 Computer Systems: A Programmer's Perspective [ \u8c46\u74e3 ] Principles of Computer System Design: An Introduction [ \u8c46\u74e3 ] \u64cd\u4f5c\u7cfb\u7edf \u73b0\u4ee3\u64cd\u4f5c\u7cfb\u7edf: \u539f\u7406\u4e0e\u5b9e\u73b0 [ \u8c46\u74e3 ] Operating Systems: Three Easy Pieces [ \u8c46\u74e3 ] Modern Operating Systems [ \u8c46\u74e3 ] Operating Systems: Principles and Practice [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u7f51\u7edc Computer Networks: A Systems Approach [ \u8c46\u74e3 ] Computer Networking: A Top-Down Approach [ \u8c46\u74e3 ] \u5206\u5e03\u5f0f\u7cfb\u7edf Patterns of Distributed System (Blog) Distributed Systems for Fun and Profit (Blog) Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems [ \u8c46\u74e3 ] \u6570\u636e\u5e93\u7cfb\u7edf Architecture of a Database System [ \u8c46\u74e3 ] Readings in Database Systems [ \u8c46\u74e3 ] Database System Concepts [ \u8c46\u74e3 ] \u7f16\u8bd1\u539f\u7406 Engineering a Compiler [ \u8c46\u74e3 ] Compilers: Principles, Techniques, and Tools [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u7f16\u7a0b\u8bed\u8a00 \u8ba1\u7b97\u673a\u7a0b\u5e8f\u7684\u6784\u9020\u548c\u89e3\u91ca [ \u8c46\u74e3 ] Essentials of Programming Languages [ \u8c46\u74e3 ] Practical Foundations for Programming Languages [ \u8c46\u74e3 ] Software Foundations [ \u8c46\u74e3 ] [ \u5317\u5927\u76f8\u5173\u8bfe\u7a0b ] Types and Programming Languages [ \u8c46\u74e3 ] [ \u5317\u5927\u76f8\u5173\u8bfe\u7a0b ] \u4f53\u7cfb\u7ed3\u6784 \u8d85\u6807\u91cf\u5904\u7406\u5668\u8bbe\u8ba1: Superscalar RISC Processor Design [ \u8c46\u74e3 ] Computer Organization and Design RISC-V Edition [ \u8c46\u74e3 ] Computer Organization and Design: The Hardware/Software Interface [ \u8c46\u74e3 ] Computer Architecture: A Quantitative Approach [ \u8c46\u74e3 ] \u7406\u8bba\u8ba1\u7b97\u673a\u79d1\u5b66 Introduction to the Theory of Computation [ \u8c46\u74e3 ] \u5bc6\u7801\u5b66 Cryptography Engineering: Design Principles and Practical Applications [ \u8c46\u74e3 ] Introduction to Modern Cryptography [ \u8c46\u74e3 ] \u9006\u5411\u5de5\u7a0b \u9006\u5411\u5de5\u7a0b\u6838\u5fc3\u539f\u7406 [ \u8c46\u74e3 ] \u52a0\u5bc6\u4e0e\u89e3\u5bc6 [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u56fe\u5f62\u5b66 Monte Carlo theory, methods and examples Advanced Global Illumination [ \u8c46\u74e3 ] Fundamentals of Computer Graphics [ \u8c46\u74e3 ] Fluid Simulation for Computer Graphics [ \u8c46\u74e3 ] Physically Based Rendering: From Theory To Implementation [ \u8c46\u74e3 ] Real-Time Rendering [ \u8c46\u74e3 ] \u6e38\u620f\u5f15\u64ce \u6e38\u620f\u7f16\u7a0b\u6a21\u5f0f: Game Programming Patterns [ \u8c46\u74e3 ] \u5b9e\u65f6\u78b0\u649e\u68c0\u6d4b\u7b97\u6cd5\u6280\u672f [ \u8c46\u74e3 ] Game AI Pro Series [ \u8c46\u74e3 ] Artificial Intelligence for Games [ \u8c46\u74e3 ] Game Engine Architecture [ \u8c46\u74e3 ] Game Programming Gems Series [ \u8c46\u74e3 ] \u8f6f\u4ef6\u5de5\u7a0b Software Engineering at Google [ \u8c46\u74e3 ] \u8bbe\u8ba1\u6a21\u5f0f \u8bbe\u8ba1\u6a21\u5f0f: \u53ef\u590d\u7528\u9762\u5411\u5bf9\u8c61\u8f6f\u4ef6\u7684\u57fa\u7840 [ \u8c46\u74e3 ] \u5927\u8bdd\u8bbe\u8ba1\u6a21\u5f0f [ \u8c46\u74e3 ] Head First \u8bbe\u8ba1\u6a21\u5f0f [ \u8c46\u74e3 ] \u6df1\u5ea6\u5b66\u4e60 \u52a8\u624b\u5b66\u6df1\u5ea6\u5b66\u4e60 [ \u8c46\u74e3 ] \u795e\u7ecf\u7f51\u7edc\u4e0e\u6df1\u5ea6\u5b66\u4e60 [ \u8c46\u74e3 ] \u6df1\u5ea6\u5b66\u4e60\u5165\u95e8 [ \u8c46\u74e3 ] \u7b80\u5355\u7c97\u66b4 TensorFlow 2 (Tutorial) Speech and Language Processing [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u89c6\u89c9 Multiple View Geometry in Computer Vision [ \u8c46\u74e3 ] \u673a\u5668\u4eba Probabilistic Robotics [ \u8c46\u74e3 ] \u9762\u8bd5 \u5251\u6307 Offer\uff1a\u540d\u4f01\u9762\u8bd5\u5b98\u7cbe\u8bb2\u5178\u578b\u7f16\u7a0b\u9898 [ \u8c46\u74e3 ] Cracking The Coding Interview [ \u8c46\u74e3 ]","title":"\u597d\u4e66\u63a8\u8350"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_1","text":"\u7531\u4e8e\u7248\u6743\u539f\u56e0\uff0c\u4e0b\u9762\u5217\u4e3e\u7684\u56fe\u4e66\u4e2d\u9664\u4e86\u5f00\u6e90\u8d44\u6e90\u63d0\u4f9b\u4e86\u94fe\u63a5\uff0c\u5176\u4ed6\u7684\u8d44\u6e90\u8bf7\u5927\u5bb6\u81ea\u884c\u901a\u8fc7 libgen \u6216 z-lib \u67e5\u627e\u3002","title":"\u597d\u4e66\u63a8\u8350"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_2","text":"Free Programming Books : \u5f00\u6e90\u7f16\u7a0b\u4e66\u7c4d\u8d44\u6e90\u6c47\u603b CS Textbook Recommendations : \u8ba1\u7b97\u673a\u79d1\u5b66\u65b9\u5411\u63a8\u8350\u6559\u6750\u5217\u8868 C Book Guide and List : C\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 C++ Book Guide and List : C++\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 Python Book Guide and List : 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Distributed System (Blog) Distributed Systems for Fun and Profit (Blog) Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems [ \u8c46\u74e3 ]","title":"\u5206\u5e03\u5f0f\u7cfb\u7edf"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_7","text":"Architecture of a Database System [ \u8c46\u74e3 ] Readings in Database Systems [ \u8c46\u74e3 ] Database System Concepts [ \u8c46\u74e3 ]","title":"\u6570\u636e\u5e93\u7cfb\u7edf"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_8","text":"Engineering a Compiler [ \u8c46\u74e3 ] Compilers: Principles, Techniques, and Tools [ \u8c46\u74e3 ]","title":"\u7f16\u8bd1\u539f\u7406"},{"location":"%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_9","text":"\u8ba1\u7b97\u673a\u7a0b\u5e8f\u7684\u6784\u9020\u548c\u89e3\u91ca [ \u8c46\u74e3 ] Essentials of Programming Languages [ \u8c46\u74e3 ] Practical Foundations for Programming Languages [ \u8c46\u74e3 ] Software Foundations [ \u8c46\u74e3 ] [ 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Bryant \u6559\u6388\u6267\u7b14\uff0c\u4e5f\u5373\u6240\u8c13\u7684 CSAPP\u3002\u8fd9\u4e5f\u662f\u6211\u7b2c\u4e00\u672c\u8ba4\u8ba4\u771f\u771f\u4e00\u9875\u4e00\u9875\u8bfb\u8fc7\u53bb\u7684\u8ba1\u7b97\u673a\u6559\u6750\uff0c\u867d\u7136\u5f88\u96be\u5543\uff0c\u4f46\u7740\u5b9e\u6536\u83b7\u826f\u591a\u3002 \u5317\u5927\u8d2d\u4e70\u4e86\u8fd9\u95e8\u8bfe\u7684\u7248\u6743\u5e76\u5f00\u8bbe\u4e86 Introduction to Computer System \u8fd9\u95e8\u8bfe\uff0c\u4f46\u5176\u5b9e CSAPP \u6240\u6709\u7684\u8bfe\u7a0b\u8d44\u6e90\u548c\u5b9e\u9a8c\u4ee3\u7801\u90fd\u80fd\u5728\u5b83\u7684\u5b98\u65b9\u4e3b\u9875\u4e0a\u8bbf\u95ee\u5230\uff08\u5177\u4f53\u53c2\u89c1\u4e0b\u65b9\u94fe\u63a5\uff09\u3002 \u8fd9\u95e8\u8bfe\u7531\u4e8e\u8fc7\u4e8e\u51fa\u540d\uff0c\u5168\u4e16\u754c\u7684\u7801\u519c\u4e89\u76f8\u5b66\u4e60\uff0c\u5bfc\u81f4\u5176 Project 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\u5199\u4f5c\u4e1a\u4e5f\u662f\u4e00\u4e2a\u4e0d\u9519\u7684\u9009\u62e9\u3002","title":"LaTeX"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/LaTeX/#latex","text":"","title":"LaTeX"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/LaTeX/#latex_1","text":"\u5982\u679c\u4f60\u9700\u8981\u5199\u8bba\u6587\uff0c\u90a3\u4e48\u8bf7\u76f4\u63a5\u8df3\u5230\u4e0b\u4e00\u8282\uff0c\u56e0\u4e3a\u4f60\u4e0d\u5b66\u4e5f\u5f97\u5b66\u3002 LaTeX \u662f\u4e00\u79cd\u57fa\u4e8e TeX \u7684\u6392\u7248\u7cfb\u7edf\uff0c\u7531\u56fe\u7075\u5956\u5f97\u4e3b Lamport \u5f00\u53d1\uff0c\u800c Tex \u5219\u662f\u7531 Knuth \u6700\u521d\u5f00\u53d1\uff0c\u8fd9\u4e24\u4f4d\u90fd\u662f\u8ba1\u7b97\u673a\u754c\u7684\u5de8\u64d8\u3002\u5f53\u7136\u5f00\u53d1\u8005\u5f3a\u5e76\u4e0d\u662f\u6211\u4eec\u5b66\u4e60 LaTeX \u7684\u7406\u7531\uff0cLaTeX \u548c\u5e38\u89c1\u7684\u6240\u89c1\u5373\u6240\u5f97\u7684 Word 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\u73af\u5883\u51fa\u73b0\u4e86\u95ee\u9898\uff0c\u53ef\u4ee5\u8003\u8651\u4f7f\u7528 Overleaf \u8fd9\u4e2a\u5728\u7ebf LaTeX \u7f16\u8f91\u7f51\u7ad9\u3002\u7ad9\u5185\u4e0d\u4ec5\u6709\u5404\u79cd\u5404\u6837\u7684 LaTeX \u6a21\u7248\u4f9b\u4f60\u9009\u62e9\uff0c\u8fd8\u514d\u53bb\u4e86\u73af\u5883\u914d\u7f6e\u7684\u96be\u9898\u3002 \u9605\u8bfb\u4e0b\u9762\u4e09\u7bc7 Tutorial: Part-1 , Part-2 , Part-3 \u3002 \u5b66\u4e60 LaTeX \u6700\u597d\u7684\u65b9\u5f0f\u5f53\u7136\u662f\u5199\u8bba\u6587\uff0c\u4e0d\u8fc7\u4ece\u4e00\u95e8\u6570\u5b66\u8bfe\u5165\u624b\u7528 LaTeX \u5199\u4f5c\u4e1a\u4e5f\u662f\u4e00\u4e2a\u4e0d\u9519\u7684\u9009\u62e9\u3002","title":"\u5982\u4f55\u5b66\u4e60 LaTeX"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/","text":"Vim \u4e3a\u4ec0\u4e48\u5b66\u4e60 Vim \u5728\u6211\u770b\u6765 Vim \u7f16\u8f91\u5668\u6709\u5982\u4e0b\u7684\u597d\u5904\uff1a \u8ba9\u4f60\u7684\u6574\u4e2a\u5f00\u53d1\u8fc7\u7a0b\u624b\u6307\u4e0d\u9700\u8981\u79bb\u5f00\u952e\u76d8\uff0c\u800c\u4e14\u5149\u6807\u7684\u79fb\u52a8\u4e0d\u9700\u8981\u65b9\u5411\u952e\u4f7f\u5f97\u4f60\u7684\u624b\u6307\u4e00\u76f4\u5904\u5728\u6253\u5b57\u7684\u6700\u4f73\u4f4d\u7f6e\u3002 \u65b9\u4fbf\u7684\u6587\u4ef6\u5207\u6362\u4ee5\u53ca\u9762\u677f\u63a7\u5236\u53ef\u4ee5\u8ba9\u4f60\u540c\u65f6\u5f00\u53d1\u591a\u4efd\u6587\u4ef6\u751a\u81f3\u540c\u4e00\u4e2a\u6587\u4ef6\u7684\u4e0d\u540c\u4f4d\u7f6e\u3002 Vim \u7684\u5b8f\u64cd\u4f5c\u53ef\u4ee5\u6279\u91cf\u5316\u5904\u7406\u91cd\u590d\u64cd\u4f5c\uff08\u4f8b\u5982\u591a\u884c tab\uff0c\u6279\u91cf\u52a0\u53cc\u5f15\u53f7\u7b49\u7b49\uff09 Vim \u662f\u5f88\u591a\u670d\u52a1\u5668\u81ea\u5e26\u7684\u547d\u4ee4\u884c\u7f16\u8f91\u5668\uff0c\u5f53\u4f60\u901a\u8fc7 ssh \u8fde\u63a5\u8fdc\u7a0b\u670d\u52a1\u5668\u4e4b\u540e\uff0c\u7531\u4e8e\u6ca1\u6709\u56fe\u5f62\u754c\u9762\uff0c\u53ea\u80fd\u5728\u547d\u4ee4\u884c\u91cc\u8fdb\u884c\u5f00\u53d1\uff08\u5f53\u7136\u73b0\u5728\u5f88\u591a IDE \u5982 VS Code \u63d0\u4f9b\u4e86 ssh \u63d2\u4ef6\u53ef\u4ee5\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff09\u3002 \u5f02\u5e38\u4e30\u5bcc\u7684\u63d2\u4ef6\u751f\u6001\uff0c\u8ba9\u4f60\u62e5\u6709\u4e16\u754c\u4e0a\u6700\u82b1\u91cc\u80e1\u54e8\u7684\u547d\u4ee4\u884c\u7f16\u8f91\u5668\u3002 \u5982\u4f55\u5b66\u4e60 Vim \u4e0d\u5e78\u7684\u662f Vim \u7684\u5b66\u4e60\u66f2\u7ebf\u786e\u5b9e\u76f8\u5f53\u9661\u5ced\uff0c\u6211\u82b1\u4e86\u597d\u51e0\u4e2a\u661f\u671f\u624d\u6162\u6162\u9002\u5e94\u4e86\u7528 Vim \u8fdb\u884c\u5f00\u53d1\u7684\u8fc7\u7a0b\u3002\u6700\u5f00\u59cb\u4f60\u4f1a\u89c9\u5f97\u975e\u5e38\u4e0d\u9002\u5e94\uff0c\u4f46\u4e00\u65e6\u71ac\u8fc7\u4e86\u521d\u59cb\u9636\u6bb5\uff0c\u76f8\u4fe1\u6211\uff0c\u4f60\u4f1a\u7231\u4e0a Vim\u3002 Vim \u7684\u5b66\u4e60\u8d44\u6599\u6d69\u5982\u70df\u6d77\uff0c\u4f46\u638c\u63e1\u5b83\u6700\u597d\u7684\u65b9\u5f0f\u8fd8\u662f\u5c06\u5b83\u7528\u5728\u65e5\u5e38\u7684\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u800c\u4e0d\u662f\u4e00\u4e0a\u6765\u5c31\u53bb\u5b66\u5404\u79cd\u82b1\u91cc\u80e1\u54e8\u7684\u9ad8\u7ea7 Vim \u6280\u5de7\u3002\u4e2a\u4eba\u63a8\u8350\u7684\u5b66\u4e60\u8def\u7ebf\u5982\u4e0b\uff1a \u5148\u9605\u8bfb \u8fd9\u7bc7 tutorial \uff0c\u638c\u63e1\u57fa\u672c\u7684 Vim \u6982\u5ff5\u548c\u4f7f\u7528\u65b9\u5f0f\u3002 \u7528 Vim \u81ea\u5e26\u7684 vimtutor \u8fdb\u884c\u7ec3\u4e60\uff0c\u5b89\u88c5\u5b8c Vim \u4e4b\u540e\u76f4\u63a5\u5728\u547d\u4ee4\u884c\u91cc\u8f93\u5165 vimtutor \u5373\u53ef\u8fdb\u5165\u7ec3\u4e60\u7a0b\u5e8f\u3002 \u6700\u540e\u5c31\u662f\u5f3a\u8feb\u81ea\u5df1\u4f7f\u7528 Vim \u8fdb\u884c\u5f00\u53d1\uff0cIDE \u91cc\u53ef\u4ee5\u5b89\u88c5 Vim \u63d2\u4ef6\u3002 \u7b49\u4f60\u5b8c\u5168\u9002\u5e94 Vim \u4e4b\u540e\u65b0\u7684\u4e16\u754c\u4fbf\u5411\u4f60\u655e\u5f00\u4e86\u5927\u95e8\uff0c\u4f60\u53ef\u4ee5\u6309\u9700\u914d\u7f6e\u81ea\u5df1\u7684 Vim\uff08\u4fee\u6539 .vimrc \u6587\u4ef6\uff09\uff0c\u7f51\u4e0a\u6709\u6570\u4e0d\u80dc\u6570\u7684\u8d44\u6e90\u53ef\u4ee5\u501f\u9274\u3002 \u63a8\u8350\u53c2\u8003\u8d44\u6599 Neil, Drew. Practical Vim: Edit Text at the Speed of Thought. N.p., Pragmatic Bookshelf, 2015. Neil, Drew. Modern Vim: Craft Your Development Environment with Vim 8 and Neovim. United States, Pragmatic Bookshelf.","title":"Vim"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#vim","text":"","title":"Vim"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#vim_1","text":"\u5728\u6211\u770b\u6765 Vim \u7f16\u8f91\u5668\u6709\u5982\u4e0b\u7684\u597d\u5904\uff1a \u8ba9\u4f60\u7684\u6574\u4e2a\u5f00\u53d1\u8fc7\u7a0b\u624b\u6307\u4e0d\u9700\u8981\u79bb\u5f00\u952e\u76d8\uff0c\u800c\u4e14\u5149\u6807\u7684\u79fb\u52a8\u4e0d\u9700\u8981\u65b9\u5411\u952e\u4f7f\u5f97\u4f60\u7684\u624b\u6307\u4e00\u76f4\u5904\u5728\u6253\u5b57\u7684\u6700\u4f73\u4f4d\u7f6e\u3002 \u65b9\u4fbf\u7684\u6587\u4ef6\u5207\u6362\u4ee5\u53ca\u9762\u677f\u63a7\u5236\u53ef\u4ee5\u8ba9\u4f60\u540c\u65f6\u5f00\u53d1\u591a\u4efd\u6587\u4ef6\u751a\u81f3\u540c\u4e00\u4e2a\u6587\u4ef6\u7684\u4e0d\u540c\u4f4d\u7f6e\u3002 Vim \u7684\u5b8f\u64cd\u4f5c\u53ef\u4ee5\u6279\u91cf\u5316\u5904\u7406\u91cd\u590d\u64cd\u4f5c\uff08\u4f8b\u5982\u591a\u884c tab\uff0c\u6279\u91cf\u52a0\u53cc\u5f15\u53f7\u7b49\u7b49\uff09 Vim \u662f\u5f88\u591a\u670d\u52a1\u5668\u81ea\u5e26\u7684\u547d\u4ee4\u884c\u7f16\u8f91\u5668\uff0c\u5f53\u4f60\u901a\u8fc7 ssh \u8fde\u63a5\u8fdc\u7a0b\u670d\u52a1\u5668\u4e4b\u540e\uff0c\u7531\u4e8e\u6ca1\u6709\u56fe\u5f62\u754c\u9762\uff0c\u53ea\u80fd\u5728\u547d\u4ee4\u884c\u91cc\u8fdb\u884c\u5f00\u53d1\uff08\u5f53\u7136\u73b0\u5728\u5f88\u591a IDE \u5982 VS Code \u63d0\u4f9b\u4e86 ssh \u63d2\u4ef6\u53ef\u4ee5\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff09\u3002 \u5f02\u5e38\u4e30\u5bcc\u7684\u63d2\u4ef6\u751f\u6001\uff0c\u8ba9\u4f60\u62e5\u6709\u4e16\u754c\u4e0a\u6700\u82b1\u91cc\u80e1\u54e8\u7684\u547d\u4ee4\u884c\u7f16\u8f91\u5668\u3002","title":"\u4e3a\u4ec0\u4e48\u5b66\u4e60 Vim"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#vim_2","text":"\u4e0d\u5e78\u7684\u662f Vim \u7684\u5b66\u4e60\u66f2\u7ebf\u786e\u5b9e\u76f8\u5f53\u9661\u5ced\uff0c\u6211\u82b1\u4e86\u597d\u51e0\u4e2a\u661f\u671f\u624d\u6162\u6162\u9002\u5e94\u4e86\u7528 Vim \u8fdb\u884c\u5f00\u53d1\u7684\u8fc7\u7a0b\u3002\u6700\u5f00\u59cb\u4f60\u4f1a\u89c9\u5f97\u975e\u5e38\u4e0d\u9002\u5e94\uff0c\u4f46\u4e00\u65e6\u71ac\u8fc7\u4e86\u521d\u59cb\u9636\u6bb5\uff0c\u76f8\u4fe1\u6211\uff0c\u4f60\u4f1a\u7231\u4e0a Vim\u3002 Vim \u7684\u5b66\u4e60\u8d44\u6599\u6d69\u5982\u70df\u6d77\uff0c\u4f46\u638c\u63e1\u5b83\u6700\u597d\u7684\u65b9\u5f0f\u8fd8\u662f\u5c06\u5b83\u7528\u5728\u65e5\u5e38\u7684\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u800c\u4e0d\u662f\u4e00\u4e0a\u6765\u5c31\u53bb\u5b66\u5404\u79cd\u82b1\u91cc\u80e1\u54e8\u7684\u9ad8\u7ea7 Vim \u6280\u5de7\u3002\u4e2a\u4eba\u63a8\u8350\u7684\u5b66\u4e60\u8def\u7ebf\u5982\u4e0b\uff1a \u5148\u9605\u8bfb \u8fd9\u7bc7 tutorial \uff0c\u638c\u63e1\u57fa\u672c\u7684 Vim \u6982\u5ff5\u548c\u4f7f\u7528\u65b9\u5f0f\u3002 \u7528 Vim \u81ea\u5e26\u7684 vimtutor \u8fdb\u884c\u7ec3\u4e60\uff0c\u5b89\u88c5\u5b8c Vim \u4e4b\u540e\u76f4\u63a5\u5728\u547d\u4ee4\u884c\u91cc\u8f93\u5165 vimtutor \u5373\u53ef\u8fdb\u5165\u7ec3\u4e60\u7a0b\u5e8f\u3002 \u6700\u540e\u5c31\u662f\u5f3a\u8feb\u81ea\u5df1\u4f7f\u7528 Vim \u8fdb\u884c\u5f00\u53d1\uff0cIDE \u91cc\u53ef\u4ee5\u5b89\u88c5 Vim \u63d2\u4ef6\u3002 \u7b49\u4f60\u5b8c\u5168\u9002\u5e94 Vim \u4e4b\u540e\u65b0\u7684\u4e16\u754c\u4fbf\u5411\u4f60\u655e\u5f00\u4e86\u5927\u95e8\uff0c\u4f60\u53ef\u4ee5\u6309\u9700\u914d\u7f6e\u81ea\u5df1\u7684 Vim\uff08\u4fee\u6539 .vimrc \u6587\u4ef6\uff09\uff0c\u7f51\u4e0a\u6709\u6570\u4e0d\u80dc\u6570\u7684\u8d44\u6e90\u53ef\u4ee5\u501f\u9274\u3002","title":"\u5982\u4f55\u5b66\u4e60 Vim"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#_1","text":"Neil, Drew. Practical Vim: Edit Text at the Speed of Thought. N.p., Pragmatic Bookshelf, 2015. Neil, Drew. Modern Vim: Craft Your Development Environment with Vim 8 and Neovim. United States, Pragmatic Bookshelf.","title":"\u63a8\u8350\u53c2\u8003\u8d44\u6599"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/thesis/","text":"\u6bd5\u4e1a\u8bba\u6587 \u4e3a\u4ec0\u4e48\u5199\u8fd9\u4efd\u6559\u7a0b 2022\u5e74\uff0c\u6211\u672c\u79d1\u6bd5\u4e1a\u4e86\u3002\u5728\u5f00\u59cb\u52a8\u624b\u5199\u6bd5\u4e1a\u8bba\u6587\u7684\u65f6\u5019\uff0c\u6211\u5c34\u5c2c\u5730\u53d1\u73b0\uff0c\u6211\u5bf9 Word \u7684\u638c\u63e1\u7a0b\u5ea6\u4ec5\u9650\u4e8e\u8c03\u8282\u5b57\u4f53\u3001\u4fdd\u5b58\u5bfc\u51fa\u8fd9\u4e9b\u50bb\u74dc\u529f\u80fd\u3002\u66fe\u60f3\u8f6c\u6218 Latex\uff0c\u4f46\u8bba\u6587\u7684\u6bb5\u843d\u683c\u5f0f\u8981\u6c42\u8c03\u6574\u8d77\u6765\u8fd8\u662f\u7528 Word 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\uff0c\u5e76\u6309\u7167\u5176\u8981\u6c42\u5236\u4f5c\u4e86 \u4e00\u4efd\u6a21\u7248 \uff0c\u5927\u5bb6\u9700\u8981\u7684\u8bdd\u81ea\u53d6\uff0c\u672c\u4eba\u4e0d\u627f\u62c5\u65e0\u6cd5\u6bd5\u4e1a\u7b49\u4efb\u4f55\u8d23\u4efb\u3002 \u5b66\u4e60 Word \u6392\u7248\uff1a\u5230\u8fbe\u8fd9\u4e00\u6b65\u7684\u7ae5\u978b\u5206\u4e3a\u4e24\u7c7b\uff0c\u4e00\u662f\u5df2\u7ecf\u62e5\u6709\u4e86\u5b66\u9662\u63d0\u4f9b\u7684\u6807\u51c6\u6a21\u7248\uff0c\u4e8c\u662f\u53ea\u6709\u4e00\u4efd\u865a\u65e0\u7f25\u7f08\u7684\u683c\u5f0f\u8981\u6c42\u3002\u90a3\u73b0\u5728\u5f53\u52a1\u4e4b\u6025\u5c31\u662f\u5b66\u4e60\u57fa\u7840\u7684 Word \u6392\u7248\u6280\u672f\uff0c\u5bf9\u4e8e\u524d\u8005\u53ef\u4ee5\u5b66\u4f1a\u4f7f\u7528\u6a21\u7248\uff0c\u5bf9\u4e8e\u540e\u8005\u5219\u53ef\u4ee5\u5b66\u4f1a\u5236\u4f5c\u6a21\u7248\u3002\u6b64\u65f6\u5207\u8bb0\u4e0d\u8981\u96c4\u5fc3\u52c3\u52c3\u5730\u9009\u62e9\u4e00\u4e2a\u5341\u51e0\u4e2a\u5c0f\u65f6\u7684 Word \u6559\u5b66\u89c6\u9891\u5f00\u59cb\u5934\u60ac\u6881\u9525\u523a\u80a1\uff0c\u56e0\u4e3a\u751f\u4ea7\u4e00\u4efd\u5e94\u4ed8\u6bd5\u4e1a\u7684\u5b66\u672f\u5783\u573e\u53ea\u8981\u5b66\u534a\u5c0f\u65f6\u80fd\u4e0a\u624b\u5c31\u591f\u4e86\u3002\u6211\u5f53\u65f6\u770b\u7684 \u4e00\u4e2a B \u7ad9\u7684\u6559\u5b66\u89c6\u9891 \uff0c\u77ed\u5c0f\u7cbe\u608d\u975e\u5e38\u5b9e\u7528\uff0c\u5168\u957f\u534a\u5c0f\u65f6\u6781\u901f\u5165\u95e8\u3002 \u751f\u4ea7\u5b66\u672f\u5783\u573e\uff1a\u6700\u5bb9\u6613\u7684\u4e00\u6b65\uff0c\u5927\u5bb6\u516b\u4ed9\u8fc7\u6d77\uff0c\u5404\u663e\u795e\u901a\u5427\uff0c\u795d\u5927\u5bb6\u6bd5\u4e1a\u987a\u5229\uff5e\uff5e","title":"\u5982\u4f55\u7528 Word \u5199\u6bd5\u4e1a\u8bba\u6587"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/","text":"\u5b9e\u7528\u5de5\u5177\u7bb1 \u4e0b\u8f7d\u5de5\u5177 Libgen : PDF\u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9\u3002 z-epub : ePub\u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9\u3002 bitdownloader : \u6cb9\u7ba1\u89c6\u9891\u4e0b\u8f7d\u5668\u3002 zlibrary : \u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9(\u53ef\u80fd\u9700\u8981\u7ffb\u5899)\u3002 \u8bbe\u8ba1\u5de5\u5177 excalidraw : \u4e00\u6b3e\u624b\u7ed8\u98ce\u683c\u7684\u7ed8\u56fe\u5de5\u5177\uff0c\u975e\u5e38\u9002\u5408\u7ed8\u5236\u8bfe\u7a0b\u62a5\u544a\u6216\u8005PPT\u5185\u7684\u793a\u610f\u56fe\u3002 origamiway : \u624b\u628a\u624b\u6559\u4f60\u600e\u4e48\u6298\u7eb8\u3002 thingiverse : \u56ca\u62ec\u5404\u7c7b 2D/3D \u8bbe\u8ba1\u8d44\u6e90\uff0c\u5176 STL \u6587\u4ef6\u4e0b\u8f7d\u53ef\u76f4\u63a5 3D \u6253\u5370\u3002 iconfont : \u56fd\u5185\u6700\u5927\u7684\u56fe\u6807\u548c\u63d2\u753b\u8d44\u6e90\u5e93\uff0c\u53ef\u7528\u4e8e\u5f00\u53d1\u6216\u7ed8\u5236\u7cfb\u7edf\u67b6\u6784\u56fe\u3002 turbosquid : \u53ef\u4ee5\u8d2d\u4e70\u5404\u5f0f\u5404\u6837\u7684\u6a21\u578b\u3002 \u7f16\u7a0b\u76f8\u5173 sqlfiddle : \u4e00\u4e2a\u7b80\u6613\u7684\u5728\u7ebf SQL Playground\u3002 godbolt : \u975e\u5e38\u65b9\u4fbf\u7684\u7f16\u8bd1\u5668\u63a2\u7d22\u5de5\u5177\u3002\u4f60\u53ef\u4ee5\u5199\u4e00\u6bb5 C/C++ \u4ee3\u7801\uff0c\u9009\u62e9\u4e00\u6b3e\u7f16\u8bd1\u5668\uff0c\u7136\u540e\u4fbf\u53ef\u4ee5\u89c2\u5bdf\u751f\u6210\u7684\u5177\u4f53\u6c47\u7f16\u4ee3\u7801\u3002 explainshell : \u4f60\u662f\u5426\u66fe\u4e3a\u4e00\u6bb5 shell \u4ee3\u7801\u7684\u5177\u4f53\u542b\u4e49\u611f\u5230\u56f0\u6270\uff1fmanpage \u770b\u534a\u5929\u8fd8\u662f\u4e0d\u660e\u6240\u4ee5\uff1f\u8bd5\u8bd5\u8fd9\u4e2a\u7f51\u7ad9\uff01 regex101 : \u6b63\u5219\u8868\u8fbe\u5f0f\u8c03\u8bd5\u7f51\u7ad9\uff0c\u652f\u6301\u5404\u79cd\u7f16\u7a0b\u8bed\u8a00\u7684\u5339\u914d\u6807\u51c6\u3002 typingtom : \u9488\u5bf9\u7a0b\u5e8f\u5458\u7684\u6253\u5b57\u7ec3\u4e60/\u6d4b\u901f\u7f51\u7ad9\u3002 \u5b66\u4e60\u7f51\u7ad9 HFS : \u5404\u7c7b\u8f6f\u4ef6\u6559\u7a0b\u3002 os-wiki : \u64cd\u4f5c\u7cfb\u7edf\u6280\u672f\u8d44\u6e90\u767e\u79d1\u5168\u4e66\u3002 Shadertoy : \u7f16\u5199\u5404\u5f0f\u5404\u6837\u7684 shader\u3002 \u6742\u9879 tophub : \u65b0\u95fb\u70ed\u699c\u5408\u96c6\uff08\u7efc\u5408\u4e86\u77e5\u4e4e\u3001\u5fae\u535a\u3001\u767e\u5ea6\u3001\u5fae\u4fe1\u7b49\uff09\u3002 speedtest : \u5728\u7ebf\u7f51\u7edc\u6d4b\u901f\u7f51\u7ad9\u3002 public-apis : \u516c\u5171 API \u5408\u96c6\u5217\u8868\u3002","title":"\u5b9e\u7528\u5de5\u5177\u7bb1"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_1","text":"","title":"\u5b9e\u7528\u5de5\u5177\u7bb1"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_2","text":"Libgen : PDF\u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9\u3002 z-epub : ePub\u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9\u3002 bitdownloader : \u6cb9\u7ba1\u89c6\u9891\u4e0b\u8f7d\u5668\u3002 zlibrary : \u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9(\u53ef\u80fd\u9700\u8981\u7ffb\u5899)\u3002","title":"\u4e0b\u8f7d\u5de5\u5177"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_3","text":"excalidraw : \u4e00\u6b3e\u624b\u7ed8\u98ce\u683c\u7684\u7ed8\u56fe\u5de5\u5177\uff0c\u975e\u5e38\u9002\u5408\u7ed8\u5236\u8bfe\u7a0b\u62a5\u544a\u6216\u8005PPT\u5185\u7684\u793a\u610f\u56fe\u3002 origamiway : \u624b\u628a\u624b\u6559\u4f60\u600e\u4e48\u6298\u7eb8\u3002 thingiverse : \u56ca\u62ec\u5404\u7c7b 2D/3D \u8bbe\u8ba1\u8d44\u6e90\uff0c\u5176 STL \u6587\u4ef6\u4e0b\u8f7d\u53ef\u76f4\u63a5 3D \u6253\u5370\u3002 iconfont : \u56fd\u5185\u6700\u5927\u7684\u56fe\u6807\u548c\u63d2\u753b\u8d44\u6e90\u5e93\uff0c\u53ef\u7528\u4e8e\u5f00\u53d1\u6216\u7ed8\u5236\u7cfb\u7edf\u67b6\u6784\u56fe\u3002 turbosquid : \u53ef\u4ee5\u8d2d\u4e70\u5404\u5f0f\u5404\u6837\u7684\u6a21\u578b\u3002","title":"\u8bbe\u8ba1\u5de5\u5177"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_4","text":"sqlfiddle : \u4e00\u4e2a\u7b80\u6613\u7684\u5728\u7ebf SQL Playground\u3002 godbolt : \u975e\u5e38\u65b9\u4fbf\u7684\u7f16\u8bd1\u5668\u63a2\u7d22\u5de5\u5177\u3002\u4f60\u53ef\u4ee5\u5199\u4e00\u6bb5 C/C++ \u4ee3\u7801\uff0c\u9009\u62e9\u4e00\u6b3e\u7f16\u8bd1\u5668\uff0c\u7136\u540e\u4fbf\u53ef\u4ee5\u89c2\u5bdf\u751f\u6210\u7684\u5177\u4f53\u6c47\u7f16\u4ee3\u7801\u3002 explainshell : \u4f60\u662f\u5426\u66fe\u4e3a\u4e00\u6bb5 shell \u4ee3\u7801\u7684\u5177\u4f53\u542b\u4e49\u611f\u5230\u56f0\u6270\uff1fmanpage \u770b\u534a\u5929\u8fd8\u662f\u4e0d\u660e\u6240\u4ee5\uff1f\u8bd5\u8bd5\u8fd9\u4e2a\u7f51\u7ad9\uff01 regex101 : \u6b63\u5219\u8868\u8fbe\u5f0f\u8c03\u8bd5\u7f51\u7ad9\uff0c\u652f\u6301\u5404\u79cd\u7f16\u7a0b\u8bed\u8a00\u7684\u5339\u914d\u6807\u51c6\u3002 typingtom : \u9488\u5bf9\u7a0b\u5e8f\u5458\u7684\u6253\u5b57\u7ec3\u4e60/\u6d4b\u901f\u7f51\u7ad9\u3002","title":"\u7f16\u7a0b\u76f8\u5173"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_5","text":"HFS : \u5404\u7c7b\u8f6f\u4ef6\u6559\u7a0b\u3002 os-wiki : \u64cd\u4f5c\u7cfb\u7edf\u6280\u672f\u8d44\u6e90\u767e\u79d1\u5168\u4e66\u3002 Shadertoy : \u7f16\u5199\u5404\u5f0f\u5404\u6837\u7684 shader\u3002","title":"\u5b66\u4e60\u7f51\u7ad9"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_6","text":"tophub : \u65b0\u95fb\u70ed\u699c\u5408\u96c6\uff08\u7efc\u5408\u4e86\u77e5\u4e4e\u3001\u5fae\u535a\u3001\u767e\u5ea6\u3001\u5fae\u4fe1\u7b49\uff09\u3002 speedtest : \u5728\u7ebf\u7f51\u7edc\u6d4b\u901f\u7f51\u7ad9\u3002 public-apis : \u516c\u5171 API \u5408\u96c6\u5217\u8868\u3002","title":"\u6742\u9879"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/%E7%BF%BB%E5%A2%99/","text":"\u7ffb\u5899 \u6b64\u94fe\u63a5 \u51fa\u73b0\u5728\u8fd9\u91cc\u7eaf\u5c5e\u4e8c\u8fdb\u5236 bit \u7684\u968f\u610f\u7ec4\u5408\uff0c\u4e0e\u672c\u4eba\u6beb\u65e0\u5173\u7cfb\u3002","title":"\u7ffb\u5899"},{"location":"%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/%E7%BF%BB%E5%A2%99/#_1","text":"\u6b64\u94fe\u63a5 \u51fa\u73b0\u5728\u8fd9\u91cc\u7eaf\u5c5e\u4e8c\u8fdb\u5236 bit \u7684\u968f\u610f\u7ec4\u5408\uff0c\u4e0e\u672c\u4eba\u6beb\u65e0\u5173\u7cfb\u3002","title":"\u7ffb\u5899"},{"location":"%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/CS162/","text":"CS162: Operating System \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A, CS61B, CS61C \u7f16\u7a0b\u8bed\u8a00\uff1aC, x86\u6c47\u7f16 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a200 \u5c0f\u65f6+\uff0c\u4e0a\u4e0d\u5c01\u9876 \u8fd9\u95e8\u8bfe\u8ba9\u6211\u8bb0\u5fc6\u72b9\u65b0\u7684\u6709\u4e24\u4e2a\u90e8\u5206\uff1a \u9996\u5148\u662f\u6559\u6750\uff0c\u8fd9\u672c\u4e66\u7528\u7684\u6559\u6750 Operating Systems: Principles and Practice (2nd Edition) \u4e00\u5171\u56db\u5377\uff0c\u5199\u5f97\u975e\u5e38\u6df1\u5165\u6d45\u51fa\uff0c\u5f88\u597d\u5730\u5f25\u8865\u4e86 MIT6.S081 \u5728\u7406\u8bba\u77e5\u8bc6\u4e0a\u7684\u4e9b\u8bb8\u7a7a\u767d\uff0c\u975e\u5e38\u5efa\u8bae\u5927\u5bb6\u9605\u8bfb\u3002\u76f8\u5173\u8d44\u6e90\u4f1a\u5206\u4eab\u5728\u672c\u4e66\u7684\u7ecf\u5178\u4e66\u7c4d\u63a8\u8350\u6a21\u5757\u3002 \u5176\u6b21\u662f\u8fd9\u95e8\u8bfe\u7684 Project \u2014\u2014 Pintos\u3002Pintos \u662f\u7531 Ben Pfaff \u7b49\u4eba\u5728 x86 \u5e73\u53f0\u4e0a\u7f16\u5199\u7684\u6559\u5b66\u7528\u64cd\u4f5c\u7cfb\u7edf\uff0cBen Pfaff \u751a\u81f3\u4e13\u95e8\u53d1\u4e86\u7bc7 paper \u6765\u9610\u8ff0 Pintos \u7684\u8bbe\u8ba1\u601d\u60f3\u3002 \u548c MIT \u7684 xv6 \u5c0f\u800c\u7cbe\u7684 lab \u8bbe\u8ba1\u7406\u5ff5\u4e0d\u540c\uff0cPintos \u66f4\u6ce8\u91cd\u7cfb\u7edf\u7684 Design and Implementation\u3002Pintos \u672c\u8eab\u4ec5\u4e00\u4e07\u884c\u5de6\u53f3\uff0c\u53ea\u63d0\u4f9b\u4e86\u64cd\u4f5c\u7cfb\u7edf\u6700\u57fa\u672c\u7684\u529f\u80fd\u3002\u800c 4 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\u4e4b\u540e\u6df1\u611f\u6127\u759a\uff0c\u542b\u6cea\u82b1\u4e86\u4e24\u767e\u591a\u4e70\u4e86\u4e00\u672c\u82f1\u6587\u6b63\u7248\u6536\u85cf\u3002\u4e0b\u9762\u9644\u4e0a\u6b64\u4e66\u5c01\u9762\uff0c\u5982\u679c\u4f60\u80fd\u5b8c\u5168\u7406\u89e3\u5c01\u9762\u56fe\u7684\u6570\u5b66\u542b\u4e49\uff0c\u90a3\u4f60\u5bf9\u7ebf\u6027\u4ee3\u6570\u7684\u7406\u89e3\u4e00\u5b9a\u4f1a\u8fbe\u5230\u65b0\u7684\u9ad8\u5ea6\u3002 \u914d\u5408\u6cb9\u7ba1\u6570\u5b66\u7f51\u7ea2 3Blue1Brown \u7684 \u7ebf\u6027\u4ee3\u6570\u7684\u672c\u8d28 \u7cfb\u5217\u89c6\u9891\u98df\u7528\u66f4\u4f73\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/syllabus/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1aIntroduction to Linear Algebra. Gilbert Strang \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9","title":"MIT18.06: Linear Algebra"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITLA/#mit1806-linear-algebra","text":"","title":"MIT18.06: Linear Algebra"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITLA/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u82f1\u6587 \u7f16\u7a0b\u8bed\u8a00\uff1a\u65e0 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a\u56e0\u4eba\u800c\u5f02 \u6570\u5b66\u5927\u725b Gilbert Strang \u8001\u5148\u751f\u5e74\u903e\u53e4\u7a00\u4ecd\u575a\u6301\u6388\u8bfe\uff0c\u5176\u7ecf\u5178\u6559\u6750 Introduction to Linear Algebra \u5df2\u88ab\u6e05\u534e\u91c7\u7528\u4e3a\u5b98\u65b9\u6559\u6750\u3002\u6211\u5f53\u65f6\u770b\u5b8c\u76d7\u7248 PDF \u4e4b\u540e\u6df1\u611f\u6127\u759a\uff0c\u542b\u6cea\u82b1\u4e86\u4e24\u767e\u591a\u4e70\u4e86\u4e00\u672c\u82f1\u6587\u6b63\u7248\u6536\u85cf\u3002\u4e0b\u9762\u9644\u4e0a\u6b64\u4e66\u5c01\u9762\uff0c\u5982\u679c\u4f60\u80fd\u5b8c\u5168\u7406\u89e3\u5c01\u9762\u56fe\u7684\u6570\u5b66\u542b\u4e49\uff0c\u90a3\u4f60\u5bf9\u7ebf\u6027\u4ee3\u6570\u7684\u7406\u89e3\u4e00\u5b9a\u4f1a\u8fbe\u5230\u65b0\u7684\u9ad8\u5ea6\u3002 \u914d\u5408\u6cb9\u7ba1\u6570\u5b66\u7f51\u7ea2 3Blue1Brown \u7684 \u7ebf\u6027\u4ee3\u6570\u7684\u672c\u8d28 \u7cfb\u5217\u89c6\u9891\u98df\u7528\u66f4\u4f73\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITLA/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/syllabus/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1aIntroduction to Linear Algebra. Gilbert Strang \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/","text":"MIT Calculus Course \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u82f1\u8bed \u7f16\u7a0b\u8bed\u8a00\uff1a\u65e0 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a\u56e0\u4eba\u800c\u5f02 MIT \u7684\u5fae\u79ef\u5206\u8bfe\u7531 MIT18.01: Single Variable Calculus \u548c MIT18.02: Multivariable Calculus \u4e24\u95e8\u8bfe\u7ec4\u6210\u3002\u5bf9\u81ea\u5df1\u6570\u5b66\u57fa\u7840\u6bd4\u8f83\u81ea\u4fe1\u7684\u540c\u5b66\u53ef\u4ee5\u53ea\u770b\u8bfe\u7a0b notes\uff0c\u5199\u5f97\u975e\u5e38\u6d45\u663e\u751f\u52a8\u5e76\u4e14\u6293\u4f4f\u672c\u8d28\uff0c\u8ba9\u4f60\u4e0d\u518d\u75b2\u4e8e\u505a\u9898\u800c\u662f\u80fd\u591f\u771f\u6b63\u7aa5\u89c1\u5fae\u79ef\u5206\u7684\u672c\u8d28\u9b45\u529b\u3002 \u914d\u5408\u6cb9\u7ba1\u6570\u5b66\u7f51\u7ea2 3Blue1Brown \u7684 \u5fae\u79ef\u5206\u7684\u672c\u8d28 \u7cfb\u5217\u89c6\u9891\u98df\u7528\u66f4\u4f73\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a 18.01 , 18.02 \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a\u53c2\u89c1\u8bfe\u7a0b notes \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u4e66\u9762\u4f5c\u4e1a\u53ca\u7b54\u6848\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9","title":"MIT18.01/18.02: Calculus"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/#mit-calculus-course","text":"","title":"MIT Calculus Course"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u82f1\u8bed \u7f16\u7a0b\u8bed\u8a00\uff1a\u65e0 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a\u56e0\u4eba\u800c\u5f02 MIT \u7684\u5fae\u79ef\u5206\u8bfe\u7531 MIT18.01: Single Variable Calculus \u548c MIT18.02: Multivariable Calculus \u4e24\u95e8\u8bfe\u7ec4\u6210\u3002\u5bf9\u81ea\u5df1\u6570\u5b66\u57fa\u7840\u6bd4\u8f83\u81ea\u4fe1\u7684\u540c\u5b66\u53ef\u4ee5\u53ea\u770b\u8bfe\u7a0b notes\uff0c\u5199\u5f97\u975e\u5e38\u6d45\u663e\u751f\u52a8\u5e76\u4e14\u6293\u4f4f\u672c\u8d28\uff0c\u8ba9\u4f60\u4e0d\u518d\u75b2\u4e8e\u505a\u9898\u800c\u662f\u80fd\u591f\u771f\u6b63\u7aa5\u89c1\u5fae\u79ef\u5206\u7684\u672c\u8d28\u9b45\u529b\u3002 \u914d\u5408\u6cb9\u7ba1\u6570\u5b66\u7f51\u7ea2 3Blue1Brown \u7684 \u5fae\u79ef\u5206\u7684\u672c\u8d28 \u7cfb\u5217\u89c6\u9891\u98df\u7528\u66f4\u4f73\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a 18.01 , 18.02 \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a\u53c2\u89c1\u8bfe\u7a0b notes \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u4e66\u9762\u4f5c\u4e1a\u53ca\u7b54\u6848\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/information/","text":"MIT6.050J: Information theory and Entropy \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u65e0 \u7f16\u7a0b\u8bed\u8a00\uff1a\u65e0 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 MIT \u9762\u5411\u5927\u4e00\u65b0\u751f\u7684\u4fe1\u606f\u8bba\u5165\u95e8\u8bfe\u7a0b\uff0cPenfield \u6559\u6388\u4e13\u95e8\u4e3a\u8fd9\u95e8\u8bfe\u5199\u4e86\u4e00\u672c \u6559\u6750 \u4f5c\u4e3a\u8bfe\u7a0b notes\uff0c\u5185\u5bb9\u6df1\u5165\u6d45\u51fa\uff0c\u751f\u52a8\u6709\u8da3\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/index.htm \u8bfe\u7a0b\u6559\u6750\uff1a https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/syllabus/MIT6_050JS08_textbook.pdf \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9\uff0c\u5305\u542b\u4e66\u9762\u4f5c\u4e1a\u4e0e Matlab \u7f16\u7a0b\u4f5c\u4e1a\u3002","title":"MIT6.050J: Information theory and Entropy"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/information/#mit6050j-information-theory-and-entropy","text":"","title":"MIT6.050J: Information theory and Entropy"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/information/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u65e0 \u7f16\u7a0b\u8bed\u8a00\uff1a\u65e0 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 MIT \u9762\u5411\u5927\u4e00\u65b0\u751f\u7684\u4fe1\u606f\u8bba\u5165\u95e8\u8bfe\u7a0b\uff0cPenfield \u6559\u6388\u4e13\u95e8\u4e3a\u8fd9\u95e8\u8bfe\u5199\u4e86\u4e00\u672c \u6559\u6750 \u4f5c\u4e3a\u8bfe\u7a0b notes\uff0c\u5185\u5bb9\u6df1\u5165\u6d45\u51fa\uff0c\u751f\u52a8\u6709\u8da3\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/information/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/index.htm \u8bfe\u7a0b\u6559\u6750\uff1a https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/syllabus/MIT6_050JS08_textbook.pdf \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9\uff0c\u5305\u542b\u4e66\u9762\u4f5c\u4e1a\u4e0e Matlab \u7f16\u7a0b\u4f5c\u4e1a\u3002","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/6.042J/","text":"MIT 6.042J: Mathematics for Computer Science \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1aCalculus, Linear Algebra \u7f16\u7a0b\u8bed\u8a00\uff1aPython preferred \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a50-70 \u5c0f\u65f6 MIT \u7684\u79bb\u6563\u6570\u5b66\u4ee5\u53ca\u6982\u7387\u7efc\u5408\u8bfe\u7a0b\uff0c\u5bfc\u5e08\u662f\u5927\u540d\u9f0e\u9f0e\u7684 Tom Leighton ( Akamai \u7684\u8054\u5408\u521b\u59cb\u4eba\u4e4b\u4e00)\u3002\u5b66\u5b8c\u4e4b\u540e\u5bf9\u4e8e\u540e\u7eed\u7684\u7b97\u6cd5\u5b66\u4e60\u5927\u6709\u88e8\u76ca\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1L741147VX \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/assignments/","title":"MIT 6.042J: Mathematics for Computer Science"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/6.042J/#mit-6042j-mathematics-for-computer-science","text":"","title":"MIT 6.042J: Mathematics for Computer Science"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/6.042J/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1aCalculus, Linear Algebra \u7f16\u7a0b\u8bed\u8a00\uff1aPython preferred \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a50-70 \u5c0f\u65f6 MIT \u7684\u79bb\u6563\u6570\u5b66\u4ee5\u53ca\u6982\u7387\u7efc\u5408\u8bfe\u7a0b\uff0c\u5bfc\u5e08\u662f\u5927\u540d\u9f0e\u9f0e\u7684 Tom Leighton ( Akamai 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\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://www.inference.org.uk/mackay/itila/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1rs411T71e \u8bfe\u7a0b\u6559\u6750\uff1aInformation Theory, Inference, and Learning Algorithms \u5728\u8bfe\u7a0b\u7f51\u7ad9\u53ef\u4ee5\u4e0b\u8f7d\u5230\u514d\u8d39\u7684\u7535\u5b50\u7248 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u5728\u6bcf\u4e00\u8282\u8bfe\u89c6\u9891\u7684\u6700\u540e\u4f1a\u7559\u6559\u6750\u4e0a\u7684\u8bfe\u540e\u4e60\u9898 R.I.P Prof. David MacKay","title":"The Information Theory, Patter Recognition, and Neural Networks"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/#the-information-theory-patter-recognition-and-neural-networks","text":"","title":"The Information Theory, Patter Recognition, and Neural Networks"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aCambridge \u5148\u4fee\u8981\u6c42\uff1aCalculus, Linear Algebra, Probabilities and Statistics \u7f16\u7a0b\u8bed\u8a00\uff1aAnything would be OK, Python preferred \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a30-50 \u5c0f\u65f6 \u5251\u6865\u5927\u5b66 Sir David MacKay \u6559\u6388\u7684\u4fe1\u606f\u8bba\u8bfe\u7a0b\u3002\u6559\u6388\u662f\u4e00\u4f4d\u5341\u5206\u7cbe\u901a\u4fe1\u606f\u8bba\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u5b66\u8005\uff0c\u8bfe\u7a0b\u5bf9\u5e94\u6559\u6750\u4e5f\u662f\u4fe1\u606f\u8bba\u9886\u57df\u7684\u4e00\u90e8\u7ecf\u5178\u8457\u4f5c\u3002\u53ef\u60dc\u5929\u5992\u82f1\u624d...","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://www.inference.org.uk/mackay/itila/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1rs411T71e \u8bfe\u7a0b\u6559\u6750\uff1aInformation Theory, Inference, and Learning Algorithms \u5728\u8bfe\u7a0b\u7f51\u7ad9\u53ef\u4ee5\u4e0b\u8f7d\u5230\u514d\u8d39\u7684\u7535\u5b50\u7248 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\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://stanford.edu/class/ee364a/index.html \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1aD4y1Q7aW \u8bfe\u7a0b\u6559\u6750\uff1a Convex Optimization \u8bfe\u7a0b\u4f5c\u4e1a\uff1a9 \u4e2a Python \u7f16\u7a0b\u4f5c\u4e1a \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/Standford_CVX101 - GitHub \u4e2d\u3002","title":"Standford EE364A: Convex Optimization"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/#stanford-ee364a-convex-optimization","text":"","title":"Stanford EE364A: Convex Optimization"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1aPython\uff0c\u5fae\u79ef\u5206\uff0c\u7ebf\u6027\u4ee3\u6570\uff0c\u6982\u7387\u8bba\uff0c\u6570\u503c\u5206\u6790 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 Stephen Boyd \u6559\u6388\u662f\u51f8\u4f18\u5316\u9886\u57df\u7684\u5927\u725b\uff0c\u5176\u7f16\u5199\u7684 Convex Optimization \u8fd9\u672c\u6559\u6750\u88ab\u4f17\u591a\u540d\u6821\u91c7\u7528\u3002\u53e6\u5916\u5176\u7814\u7a76\u56e2\u961f\u8fd8\u4e13\u95e8\u5f00\u53d1\u4e86\u4e00\u4e2a\u7528\u4e8e\u6c42\u89e3\u5e38\u89c1\u51f8\u4f18\u5316\u95ee\u9898\u7684\u7f16\u7a0b\u6846\u67b6\uff0c\u652f\u6301 Python, Julia \u7b49\u4e3b\u6d41\u7f16\u7a0b\u8bed\u8a00\uff0c\u5176\u8bfe\u7a0b\u4f5c\u4e1a\u4e5f\u662f\u91c7\u7528\u8fd9\u4e2a\u7f16\u7a0b\u6846\u67b6\u53bb\u89e3\u51b3\u5b9e\u9645\u751f\u6d3b\u5f53\u4e2d\u7684\u51f8\u4f18\u5316\u95ee\u9898\u3002 \u5728\u5b9e\u9645\u8fd0\u7528\u5f53\u4e2d\uff0c\u4f60\u4f1a\u6df1\u523b\u4f53\u4f1a\u5230\u5bf9\u4e8e\u540c\u4e00\u4e2a\u95ee\u9898\uff0c\u5efa\u6a21\u8fc7\u7a0b\u4e2d\u4e00\u4e2a\u7ec6\u5c0f\u7684\u6539\u53d8\uff0c\u5176\u65b9\u7a0b\u7684\u6c42\u89e3\u96be\u5ea6\u4f1a\u6709\u5929\u58e4\u4e4b\u522b\uff0c\u5982\u4f55\u8ba9\u4f60\u5efa\u6a21\u7684\u65b9\u7a0b\u662f\u201c\u51f8\u201d\u7684\uff0c\u662f\u4e00\u95e8\u827a\u672f\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://stanford.edu/class/ee364a/index.html \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1aD4y1Q7aW \u8bfe\u7a0b\u6559\u6750\uff1a Convex Optimization \u8bfe\u7a0b\u4f5c\u4e1a\uff1a9 \u4e2a Python \u7f16\u7a0b\u4f5c\u4e1a","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/#_3","text":"@PKUFlyingPig 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Join\uff0c\u7edf\u8ba1\u4fe1\u606f\u4ee5\u53ca\u4ee3\u4ef7\u4f30\u8ba1\uff0c\u5b50\u67e5\u8be2\u5b9e\u73b0\uff0cAgg\uff0cGroup By \u7684\u5b9e\u73b0\u7b49\u3002\u9664\u6b64\u4e4b\u5916\uff0c\u8fd8\u6709 B+\u6811\uff0cWAL \u76f8\u5173\u5b9e\u9a8c\u3002\u672c\u95e8\u8bfe\u7a0b\u9002\u5408\u5728\u5b66\u5b8c CMU15-445 \u8bfe\u7a0b\u4e4b\u540e\uff0c\u5bf9\u67e5\u8be2\u4f18\u5316\u76f8\u5173\u5185\u5bb9\u6709\u5174\u8da3\u7684\u540c\u5b66\u3002 \u4e0b\u9762\u4ecb\u7ecd\u4e00\u4e0b\u8fd9\u95e8\u8bfe\u7684\u524d 3 \u4e2a Assignment \u4e5f\u5c31\u662f\u5b9e\u9a8c Lab \u6240\u8981\u5b9e\u73b0\u7684\u529f\u80fd\uff1a Assignment1 \u4e3a NanoDB \u63d0\u4f9b delete\uff0cupdate \u8bed\u53e5\u7684\u652f\u6301\u3002 \u4e3a Buffer Pool Manager \u6dfb\u52a0\u5408\u9002\u7684 pin/unpin \u4ee3\u7801\u3002 \u63d0\u5347 insert \u8bed\u53e5\u7684\u6027\u80fd\uff0c \u540c\u65f6\u4e0d\u4f7f\u6570\u636e\u5e93\u6587\u4ef6\u5927\u5c0f\u8fc7\u5206\u81a8\u80c0\u3002 Assignment2 \u5b9e\u73b0\u4e00\u4e2a\u7b80\u5355\u7684\u8ba1\u5212\u751f\u6210\u5668\uff0c\u5c06\u5404\u79cd\u5df2\u7ecf Parser \u8fc7\u7684 SQL \u8bed\u53e5\u8f6c\u5316\u4e3a\u53ef\u6267\u884c\u7684\u6267\u884c\u8ba1\u5212\u3002 \u4f7f\u7528 nested-loop join \u7b97\u6cd5\uff0c\u5b9e\u73b0\u652f\u6301 inner- and outer-join \u7684 Join \u8ba1\u5212\u8282\u70b9\u3002 \u6dfb\u52a0\u4e00\u4e9b\u5355\u5143\u6d4b\u8bd5\uff0c \u4fdd\u8bc1 inner- and outer-join \u529f\u80fd\u5b9e\u73b0\u6b63\u786e\u3002 Assignment3 \u5b8c\u6210\u6536\u96c6\u8868\u7684\u7edf\u8ba1\u4fe1\u606f\u3002 \u5b8c\u6210\u5404\u79cd\u8ba1\u5212\u8282\u70b9\u7684\u8ba1\u5212\u6210\u672c\u8ba1\u7b97\u3002 \u8ba1\u7b97\u53ef\u51fa\u73b0\u5728\u6267\u884c\u8ba1\u5212\u4e2d\u7684\u5404\u79cd\u8c13\u8bcd\u7684\u9009\u62e9\u6027\u3002 \u6839\u636e\u8c13\u8bcd\u66f4\u65b0\u8ba1\u5212\u8282\u70b9\u8f93\u51fa\u7684\u5143\u7ec4\u7edf\u8ba1\u4fe1\u606f\u3002 \u5269\u4f59 Assignment \u548c Challenges \u53ef\u4ee5\u67e5\u770b\u8bfe\u7a0b\u4ecb\u7ecd\uff0c\u63a8\u8350\u4f7f\u7528 IDEA \u6253\u5f00\u5de5\u7a0b\uff0cMaven \u6784\u5efa\uff0c\u6ce8\u610f\u65e5\u5fd7\u76f8\u5173\u914d\u7f6e\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://courses.cms.caltech.edu/cs122/ \u8bfe\u7a0b\u4ee3\u7801\uff1a https://gitlab.caltech.edu/cs122-19wi \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a7 Assignments + 2 Challenges","title":"Caltech CS122: Database System Implementation"},{"location":"%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS122/#caltech-cs-122-database-system-implementation","text":"","title":"Caltech CS 122: Database System Implementation"},{"location":"%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS122/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aCaltech \u5148\u4fee\u8981\u6c42\uff1a\u65e0 \u7f16\u7a0b\u8bed\u8a00\uff1aJava \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f 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\u8fd9\u95e8\u8bfe\u7684\u6784\u6210\u5c31\u975e\u5e38\u597d\u5730\u5951\u5408\u4e86\u4e0a\u8ff0\u4e09\u4e2a\u6b65\u9aa4\u3002\u89c2\u770b\u8bfe\u7a0b\u89c6\u9891\u5e76\u4e14\u9605\u8bfb\u6559\u6388\u7684 \u5f00\u6e90\u8bfe\u672c \u6709\u52a9\u4e8e\u4f60\u7406\u89e3\u7b97\u6cd5\u7684\u672c\u8d28\uff0c\u8ba9\u4f60\u4e5f\u53ef\u4ee5\u7528\u975e\u5e38 \u751f\u52a8\u6d45\u663e\u7684\u8bdd\u8bed\u5411\u522b\u4eba\u8bb2\u8ff0\u4e3a\u4ec0\u4e48\u8fd9\u4e2a\u7b97\u6cd5\u5f97\u957f\u8fd9\u4e2a\u6837\u5b50\u3002 \u5728\u7406\u89e3\u7b97\u6cd5\u4e4b\u540e\uff0c\u4f60\u53ef\u4ee5\u9605\u8bfb\u6559\u6388\u5bf9\u4e8e\u8bfe\u7a0b\u4e2d\u8bb2\u6388\u7684\u6240\u6709\u6570\u636e\u7ed3\u6784\u4e0e\u7b97\u6cd5\u7684 \u4ee3\u7801\u5b9e\u73b0 \u3002 \u6ce8\u610f\uff0c\u8fd9\u4e9b\u5b9e\u73b0\u53ef\u4e0d\u662f demo \u6027\u8d28\u7684\uff0c\u800c\u662f\u5de5\u4e1a\u7ea7\u7684\u9ad8\u6548\u5b9e\u73b0\uff0c\u4ece\u6ce8\u91ca\u5230\u53d8\u91cf\u547d\u540d\u90fd\u975e\u5e38\u4e25\u8c28\uff0c\u6a21\u5757\u5316\u4e5f\u505a\u5f97\u76f8\u5f53\u597d\uff0c\u662f\u8d28\u91cf\u5f88\u9ad8\u7684\u4ee3\u7801\u3002\u6211\u4ece\u8fd9\u4e9b\u4ee3\u7801\u4e2d\u6536\u83b7\u826f\u591a\u3002 \u6700\u540e\uff0c\u5c31\u662f\u8fd9\u95e8\u8bfe\u6700\u6fc0\u52a8\u4eba\u5fc3\u7684\u90e8\u5206\u4e86\uff0c10 \u4e2a\u9ad8\u8d28\u91cf\u7684 Project\uff0c\u5e76\u4e14\u5168\u90fd\u6709\u5b9e\u9645\u95ee\u9898\u7684\u80cc\u666f\u63cf\u8ff0\uff0c\u4e30\u5bcc\u7684\u6d4b\u8bd5\u6837\u4f8b\uff0c\u81ea\u52a8\u7684\u8bc4\u5206\u7cfb\u7edf\uff08\u4ee3\u7801\u98ce\u683c\u4e5f\u662f\u8bc4\u5206\u7684\u4e00\u73af\uff09\u3002\u8ba9\u4f60\u5728\u5b9e\u9645\u751f\u6d3b\u4e2d \u9886\u7565\u7b97\u6cd5\u7684\u9b45\u529b\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a Algorithm I , Algorithm II \u8bfe\u7a0b\u89c6\u9891\uff1a\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a https://algs4.cs.princeton.edu/home/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a10\u4e2aProject\uff0c\u5177\u4f53\u8981\u6c42\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/Princeton-Algorithm - GitHub \u4e2d\u3002","title":"Coursera: Algorithms I & II"},{"location":"%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/#coursera-algorithms-i-ii","text":"","title":"Coursera: Algorithms I & II"},{"location":"%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aPrinceton \u5148\u4fee\u8981\u6c42\uff1aCS61A \u7f16\u7a0b\u8bed\u8a00\uff1aJava \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a60 \u5c0f\u65f6 \u8fd9\u662f Coursera \u4e0a\u8bc4\u5206\u6700\u9ad8\u7684\u7b97\u6cd5\u8bfe\u7a0b\u3002Robert Sedgewick \u6559\u6388\u6709\u4e00\u79cd\u9b54\u529b\uff0c\u53ef\u4ee5\u5c06\u65e0\u8bba\u591a\u4e48\u590d\u6742\u7684\u7b97\u6cd5\u8bb2\u5f97\u6781\u4e3a\u751f\u52a8\u6d45\u663e\u3002\u5b9e\u4e0d\u76f8\u7792\uff0c\u56f0\u6270\u6211\u591a\u5e74\u7684 KMP \u4ee5\u53ca\u7f51\u7edc\u6d41\u7b97\u6cd5\u90fd\u662f\u5728\u8fd9\u95e8\u8bfe\u4e0a\u8ba9\u6211\u8305\u585e\u987f\u5f00\u7684\uff0c\u65f6\u9694\u4e24\u5e74\u6211\u751a\u81f3\u8fd8\u80fd\u5199\u51fa\u8fd9\u4e24\u4e2a\u7b97\u6cd5\u7684\u63a8\u5bfc\u4e0e\u8bc1\u660e\u3002 \u4f60\u662f\u5426\u89c9\u5f97\u7b97\u6cd5\u5b66\u4e86\u5c31\u5fd8\u5462\uff1f\u6211\u89c9\u5f97\u8ba9\u4f60\u5b8c\u5168\u638c\u63e1\u4e00\u4e2a\u7b97\u6cd5\u7684\u6838\u5fc3\u5728\u4e8e\u7406\u89e3\u4e09\u70b9\uff1a \u4e3a\u4ec0\u4e48\u8fd9\u4e48\u505a\uff1f\uff08\u6b63\u786e\u6027\u63a8\u5bfc\uff0c\u6291\u6216\u662f\u6574\u4e2a\u7b97\u6cd5\u7684\u6838\u5fc3\u672c\u8d28\uff09 \u5982\u4f55\u5b9e\u73b0\u5b83\uff1f\uff08\u5149\u5b66\u4e0d\u7528\u5047\u628a\u5f0f\uff09 \u7528\u5b83\u89e3\u51b3\u5b9e\u9645\u95ee\u9898\uff08\u5b66\u4ee5\u81f4\u7528\u624d\u662f\u771f\u672c\u4e8b\uff09 \u8fd9\u95e8\u8bfe\u7684\u6784\u6210\u5c31\u975e\u5e38\u597d\u5730\u5951\u5408\u4e86\u4e0a\u8ff0\u4e09\u4e2a\u6b65\u9aa4\u3002\u89c2\u770b\u8bfe\u7a0b\u89c6\u9891\u5e76\u4e14\u9605\u8bfb\u6559\u6388\u7684 \u5f00\u6e90\u8bfe\u672c \u6709\u52a9\u4e8e\u4f60\u7406\u89e3\u7b97\u6cd5\u7684\u672c\u8d28\uff0c\u8ba9\u4f60\u4e5f\u53ef\u4ee5\u7528\u975e\u5e38 \u751f\u52a8\u6d45\u663e\u7684\u8bdd\u8bed\u5411\u522b\u4eba\u8bb2\u8ff0\u4e3a\u4ec0\u4e48\u8fd9\u4e2a\u7b97\u6cd5\u5f97\u957f\u8fd9\u4e2a\u6837\u5b50\u3002 \u5728\u7406\u89e3\u7b97\u6cd5\u4e4b\u540e\uff0c\u4f60\u53ef\u4ee5\u9605\u8bfb\u6559\u6388\u5bf9\u4e8e\u8bfe\u7a0b\u4e2d\u8bb2\u6388\u7684\u6240\u6709\u6570\u636e\u7ed3\u6784\u4e0e\u7b97\u6cd5\u7684 \u4ee3\u7801\u5b9e\u73b0 \u3002 \u6ce8\u610f\uff0c\u8fd9\u4e9b\u5b9e\u73b0\u53ef\u4e0d\u662f demo \u6027\u8d28\u7684\uff0c\u800c\u662f\u5de5\u4e1a\u7ea7\u7684\u9ad8\u6548\u5b9e\u73b0\uff0c\u4ece\u6ce8\u91ca\u5230\u53d8\u91cf\u547d\u540d\u90fd\u975e\u5e38\u4e25\u8c28\uff0c\u6a21\u5757\u5316\u4e5f\u505a\u5f97\u76f8\u5f53\u597d\uff0c\u662f\u8d28\u91cf\u5f88\u9ad8\u7684\u4ee3\u7801\u3002\u6211\u4ece\u8fd9\u4e9b\u4ee3\u7801\u4e2d\u6536\u83b7\u826f\u591a\u3002 \u6700\u540e\uff0c\u5c31\u662f\u8fd9\u95e8\u8bfe\u6700\u6fc0\u52a8\u4eba\u5fc3\u7684\u90e8\u5206\u4e86\uff0c10 \u4e2a\u9ad8\u8d28\u91cf\u7684 Project\uff0c\u5e76\u4e14\u5168\u90fd\u6709\u5b9e\u9645\u95ee\u9898\u7684\u80cc\u666f\u63cf\u8ff0\uff0c\u4e30\u5bcc\u7684\u6d4b\u8bd5\u6837\u4f8b\uff0c\u81ea\u52a8\u7684\u8bc4\u5206\u7cfb\u7edf\uff08\u4ee3\u7801\u98ce\u683c\u4e5f\u662f\u8bc4\u5206\u7684\u4e00\u73af\uff09\u3002\u8ba9\u4f60\u5728\u5b9e\u9645\u751f\u6d3b\u4e2d \u9886\u7565\u7b97\u6cd5\u7684\u9b45\u529b\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a Algorithm I , Algorithm II \u8bfe\u7a0b\u89c6\u9891\uff1a\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a https://algs4.cs.princeton.edu/home/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a10\u4e2aProject\uff0c\u5177\u4f53\u8981\u6c42\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/Princeton-Algorithm - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS170/","text":"CS170: Efficient Algorithms and Intractable Problems \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61B, CS70 \u7f16\u7a0b\u8bed\u8a00\uff1aLaTeX \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a60 \u5c0f\u65f6 \u4f2f\u514b\u5229\u7684\u7b97\u6cd5\u8bbe\u8ba1\u8bfe\uff0c\u66f4\u6ce8\u91cd\u7b97\u6cd5\u7684\u7406\u8bba\u57fa\u7840\u4e0e\u590d\u6742\u5ea6\u5206\u6790\u3002\u8bfe\u7a0b\u5185\u5bb9\u6db5\u76d6\u4e86\u5206\u6cbb\u3001\u56fe\u7b97\u6cd5\u3001\u6700\u77ed\u8def\u3001\u751f\u6210\u6811\u3001\u8d2a\u5fc3\u3001\u52a8\u89c4\u3001\u5e76\u67e5\u96c6\u3001\u7ebf\u6027\u89c4\u5212\u3001\u7f51\u7edc\u6d41\u3001NP \u95ee\u9898\u3001\u968f\u673a\u7b97\u6cd5\u3001\u54c8\u5e0c\u7b97\u6cd5\u7b49\u7b49\u3002 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\u8fd9\u4e00\u4e3b\u6d41\u7684\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u6846\u67b6\u4e3a\u4f8b\uff08\u9648\u5929\u5947\u662f\u8fd9\u4e2a\u6846\u67b6\u7684\u521b\u59cb\u4eba\u4e4b\u4e00\uff09\uff0c\u805a\u7126\u4e8e\u5982\u4f55\u5c06\u5f00\u53d1\u6a21\u5f0f\u4e0b\uff08\u5982 Tensorflow, Pytorch, Jax\uff09\u7684\u5404\u7c7b\u673a\u5668\u5b66\u4e60\u6a21\u578b\uff0c\u901a\u8fc7\u4e00\u5957\u666e\u9002\u7684\u62bd\u8c61\u548c\u4f18\u5316\u7b97\u6cd5\uff0c\u53d8\u6362\u4e3a\u62e5\u6709\u66f4\u9ad8\u6027\u80fd\u5e76\u4e14\u9002\u914d\u5404\u7c7b\u5e95\u5c42\u786c\u4ef6\u7684\u90e8\u7f72\u6a21\u5f0f\u3002\u8bfe\u7a0b\u8bb2\u6388\u7684\u77e5\u8bc6\u70b9\u90fd\u662f\u76f8\u5bf9 High-Level \u7684\u5b8f\u89c2\u6982\u5ff5\uff0c\u540c\u65f6\u6bcf\u8282\u8bfe\u90fd\u4f1a\u6709\u4e00\u4e2a\u914d\u5957\u7684 Jupyter Notebook \u6765\u901a\u8fc7\u5177\u4f53\u7684\u4ee3\u7801\u8bb2\u89e3\u77e5\u8bc6\u70b9\uff0c\u56e0\u6b64\u5982\u679c\u4ece\u4e8b TVM \u76f8\u5173\u7684\u7f16\u7a0b\u5f00\u53d1\u7684\u8bdd\uff0c\u8fd9\u95e8\u8bfe\u6709\u4e30\u5bcc\u4e14\u89c4\u8303\u7684\u4ee3\u7801\u793a\u4f8b\u4ee5\u4f9b\u53c2\u8003\u3002 \u6240\u6709\u7684\u8bfe\u7a0b\u8d44\u6e90\u5168\u90e8\u5f00\u6e90\u5e76\u4e14\u6709\u4e2d\u6587\u548c\u82f1\u6587\u4e24\u4e2a\u7248\u672c\uff0cB\u7ad9\u548c\u6cb9\u7ba1\u5206\u522b\u6709\u4e2d\u6587\u548c\u82f1\u6587\u7684\u8bfe\u7a0b\u5f55\u5f71\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%B3%BB%E7%BB%9F/MLC/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://mlc.ai/summer22-zh/ \u8bfe\u7a0b\u89c6\u9891\uff1a Bilibili \u8bfe\u7a0b\u7b14\u8bb0\uff1a https://mlc.ai/zh/index.html \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://github.com/mlc-ai/notebooks/blob/main/assignment","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CMU10-708/","text":"CMU 10-708: Probabilistic Graphical Models \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aCMU \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://sailinglab.github.io/pgm-spring-2019/ \u8fd9\u4e2a\u7f51\u7ad9\u5305\u542b\u4e86\u6240\u6709\u7684\u8d44\u6e90\uff1aslides, nots, video, homework, project \u8fd9\u95e8\u8bfe\u7a0b\u662f CMU \u7684\u56fe\u6a21\u578b\u57fa\u7840 + \u8fdb\u9636\u8bfe\uff0c\u6388\u8bfe\u8001\u5e08\u4e3a Eric P. Xing\uff0c\u6db5\u76d6\u4e86\u56fe\u6a21\u578b\u57fa\u7840\uff0c\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u5728\u5f3a\u5316\u5b66\u4e60\u4e2d\u7684\u5e94\u7528\uff0c\u4ee5\u53ca\u975e\u53c2\u6570\u65b9\u6cd5\u3002\u76f8\u5f53\u786c\u6838","title":"CMU 10-708: Probabilistic Graphical Models"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CMU10-708/#cmu-10-708-probabilistic-graphical-models","text":"","title":"CMU 10-708: Probabilistic Graphical Models"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CMU10-708/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aCMU \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://sailinglab.github.io/pgm-spring-2019/ \u8fd9\u4e2a\u7f51\u7ad9\u5305\u542b\u4e86\u6240\u6709\u7684\u8d44\u6e90\uff1aslides, nots, video, homework, project \u8fd9\u95e8\u8bfe\u7a0b\u662f CMU \u7684\u56fe\u6a21\u578b\u57fa\u7840 + \u8fdb\u9636\u8bfe\uff0c\u6388\u8bfe\u8001\u5e08\u4e3a Eric P. Xing\uff0c\u6db5\u76d6\u4e86\u56fe\u6a21\u578b\u57fa\u7840\uff0c\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u5728\u5f3a\u5316\u5b66\u4e60\u4e2d\u7684\u5e94\u7528\uff0c\u4ee5\u53ca\u975e\u53c2\u6570\u65b9\u6cd5\u3002\u76f8\u5f53\u786c\u6838","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CS229M/","text":"STATS214 / CS229M: Machine Learning Theory \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Statistics \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/stats214/ \u7ecf\u5178\u5b66\u4e60\u7406\u8bba + \u6700\u65b0\u6df1\u5ea6\u5b66\u4e60\u7406\u8bba\uff0c\u975e\u5e38\u786c\u6838\u3002\u6388\u8bfe\u8001\u5e08\u4e4b\u524d\u662f Percy Liang\uff0c\u73b0\u5728\u662f Tengyu Ma","title":"Stanford STATS214 / CS229M: Machine Learning Theory"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CS229M/#stats214-cs229m-machine-learning-theory","text":"","title":"STATS214 / CS229M: Machine Learning Theory"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CS229M/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Statistics \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/stats214/ \u7ecf\u5178\u5b66\u4e60\u7406\u8bba + \u6700\u65b0\u6df1\u5ea6\u5b66\u4e60\u7406\u8bba\uff0c\u975e\u5e38\u786c\u6838\u3002\u6388\u8bfe\u8001\u5e08\u4e4b\u524d\u662f Percy Liang\uff0c\u73b0\u5728\u662f Tengyu Ma","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STA4273/","text":"STA 4273 Winter 2021: Minimizing Expectations \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aU Toronto \u5148\u4fee\u8981\u6c42\uff1aBayesian Inference, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.cs.toronto.edu/~cmaddis/courses/sta4273_w21/ \u8fd9\u662f\u4e00\u95e8\u8f83\u4e3a\u8fdb\u9636\u7684 Ph.D. \u7814\u7a76\u8bfe\u7a0b\uff0c\u6838\u5fc3\u5185\u5bb9\u662f inference \u548c control \u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u6388\u8bfe\u8001\u5e08\u4e3a Chris Maddison (AlphaGo founding member, NeurIPS 14 best paper)","title":"U Toronto STA 4273 Winter 2021: Minimizing Expectations"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STA4273/#sta-4273-winter-2021-minimizing-expectations","text":"","title":"STA 4273 Winter 2021: Minimizing Expectations"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STA4273/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aU Toronto \u5148\u4fee\u8981\u6c42\uff1aBayesian Inference, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.cs.toronto.edu/~cmaddis/courses/sta4273_w21/ \u8fd9\u662f\u4e00\u95e8\u8f83\u4e3a\u8fdb\u9636\u7684 Ph.D. \u7814\u7a76\u8bfe\u7a0b\uff0c\u6838\u5fc3\u5185\u5bb9\u662f inference \u548c control \u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u6388\u8bfe\u8001\u5e08\u4e3a Chris Maddison (AlphaGo founding member, NeurIPS 14 best paper)","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STAT8201/","text":"Columbia STAT 8201: Deep Generative Models \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aColumbia University \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Graphical Models \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://stat.columbia.edu/~cunningham/teaching/GR8201/ \u8fd9\u95e8\u8bfe\u662f\u4e00\u95e8 PhD \u8ba8\u8bba\u73ed\uff0c\u6bcf\u5468\u7684\u5185\u5bb9\u662f\u5c55\u793a + \u8ba8\u8bba\u8bba\u6587\uff0c\u6388\u8bfe\u8001\u5e08\u662f John Cunningham\u3002Deep Generative Models \uff08\u6df1\u5ea6\u751f\u6210\u6a21\u578b\uff09 \u662f\u56fe\u6a21\u578b\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u4e5f\u662f\u73b0\u4ee3\u673a\u5668\u5b66\u4e60\u6700\u91cd\u8981\u7684\u65b9\u5411\u4e4b\u4e00","title":"Columbia STAT 8201: Deep Generative Models"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STAT8201/#columbia-stat-8201-deep-generative-models","text":"","title":"Columbia STAT 8201: Deep Generative Models"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STAT8201/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aColumbia University \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Graphical Models \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://stat.columbia.edu/~cunningham/teaching/GR8201/ \u8fd9\u95e8\u8bfe\u662f\u4e00\u95e8 PhD \u8ba8\u8bba\u73ed\uff0c\u6bcf\u5468\u7684\u5185\u5bb9\u662f\u5c55\u793a + \u8ba8\u8bba\u8bba\u6587\uff0c\u6388\u8bfe\u8001\u5e08\u662f John Cunningham\u3002Deep Generative Models \uff08\u6df1\u5ea6\u751f\u6210\u6a21\u578b\uff09 \u662f\u56fe\u6a21\u578b\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u4e5f\u662f\u73b0\u4ee3\u673a\u5668\u5b66\u4e60\u6700\u91cd\u8981\u7684\u65b9\u5411\u4e4b\u4e00","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/","text":"\u673a\u5668\u5b66\u4e60\u8fdb\u9636 \u6b64\u8def\u7ebf\u56fe\u9002\u7528\u4e8e\u5df2\u7ecf\u5b66\u8fc7\u4e86\u57fa\u7840\u673a\u5668\u5b66\u4e60 (ML, NLP, CV, RL) \u7684\u540c\u5b66 (\u9ad8\u5e74\u7ea7\u672c\u79d1\u751f\u6216\u4f4e\u5e74\u7ea7\u7814\u7a76\u751f)\uff0c\u5df2\u7ecf\u53d1\u8868\u8fc7\u81f3\u5c11\u4e00\u7bc7\u9876\u4f1a\u8bba\u6587 (NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, CVPR, ICCV) \u60f3\u8981\u8d70\u673a\u5668\u5b66\u4e60\u79d1\u7814\u8def\u7ebf\u7684\u9009\u624b\u3002 \u6b64\u8def\u7ebf\u7684\u76ee\u6807\u662f\u4e3a\u8bfb\u61c2\u4e0e\u53d1\u8868\u673a\u5668\u5b66\u4e60\u9876\u4f1a\u8bba\u6587\u6253\u4e0b\u7406\u8bba\u57fa\u7840\uff0c\u7279\u522b\u662f Probabilistic Methods \u8fd9\u4e2a track \u4e0b\u7684\u6587\u7ae0 \u673a\u5668\u5b66\u4e60\u8fdb\u9636\u53ef\u80fd\u5b58\u5728\u591a\u79cd\u4e0d\u540c\u7684\u5b66\u4e60\u8def\u7ebf\uff0c\u6b64\u8def\u7ebf\u53ea\u80fd\u4ee3\u8868\u4f5c\u8005 Yao Fu \u6240\u7406\u89e3\u7684\u6700\u4f73\u8def\u5f84\uff0c\u4fa7\u91cd\u4e8e\u8d1d\u53f6\u65af\u5b66\u6d3e\u4e0b\u7684\u6982\u7387\u5efa\u6a21\u65b9\u6cd5\uff0c\u4e5f\u4f1a\u6d89\u53ca\u5230\u5404\u9879\u76f8\u5173\u5b66\u79d1\u7684\u4ea4\u53c9\u77e5\u8bc6\u3002 \u5fc5\u8bfb\u6559\u6750 PRML: Pattern Recognition and Machine Learning. Christopher Bishop \u7ecf\u5178\u8d1d\u53f6\u65af\u5b66\u6d3e\u6559\u6750 AoS: All of Statistics. Larry Wasserman \u7ecf\u5178\u9891\u7387\u5b66\u6d3e\u6559\u6750 \u6240\u4ee5\u8fd9\u4e24\u672c\u4e66\u521a\u597d\u76f8\u8f85\u76f8\u6210 \u5b57\u5178 MLAPP: Machine Learning: A Probabilistic Perspective. Kevin Murphy Convex Optimization. Stephen Boyd and Lieven Vandenberghe \u8fdb\u9636\u4e66\u7c4d W&J: Graphical Models, Exponential Families, and Variational Inference. Martin Wainwright and Michael Jordan Theory of Point Estimation. E. L. Lehmann and George Casella \u5982\u4f55\u9605\u8bfb Guidelines \u5fc5\u8bfb\u6559\u6750\u5c31\u662f\u4e00\u5b9a\u8981\u8bfb\u7684\u6559\u6750 \u5b57\u5178\u7684\u610f\u601d\u662f\uff0c\u4e00\u822c\u60c5\u51b5\u4e0b\u4e0d\u7ba1\u5b83\uff0c\u4f46\u5f53\u9047\u5230\u4e86\u4e0d\u61c2\u7684\u6982\u5ff5\u7684\u65f6\u5019\uff0c\u5c31\u53bb\u5b57\u5178\u91cc\u9762\u67e5\uff08\u800c\u4e0d\u662f\u7ef4\u57fa\u767e\u79d1\uff09 \u8fdb\u9636\u4e66\u7c4d\u5148\u4e0d\u8bfb\uff0c\u5148\u8bfb\u5b8c\u5fc5\u8bfb\u4e66\u7c4d\u3002\u5fc5\u8bfb\u4e66\u7c4d\u4e00\u822c\u90fd\u662f\u8981\u524d\u524d\u540e\u540e\u53cd\u590d\u770b\u8fc7 N \u904d\u624d\u7b97\u8bfb\u5b8c \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u6700\u91cd\u8981\u7684\u8bfb\u6cd5\u5c31\u662f\u5bf9\u6bd4\u9605\u8bfb (contrastive-comparative reading)\uff1a\u540c\u65f6\u6253\u5f00\u4e24\u672c\u4e66\u8bb2\u540c\u4e00\u4e3b\u9898\u7684\u7ae0\u8282\uff0c\u7136\u540e\u5bf9\u6bd4\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u5c3d\u91cf\u53bb\u56de\u60f3\u4e4b\u524d\u8bfb\u8fc7\u7684\u8bba\u6587\uff0c\u6bd4\u8f83\u8bba\u6587\u548c\u6559\u6750\u7684\u76f8\u540c\u70b9\u4e0e\u4e0d\u540c\u70b9 \u57fa\u7840\u8def\u5f84 \u5148\u8bfb AoS \u7b2c\u516d\u7ae0: Models, Statistical Inference and Learning\uff0c\u8fd9\u4e00\u90e8\u5206\u662f\u6700\u57fa\u7840\u7684\u79d1\u666e \u7136\u540e\u8bfb PRML \u7b2c 10, 11 \u7ae0 \u7b2c 10 \u7ae0\u7684\u5185\u5bb9\u662f Variational Inference, \u7b2c 11 \u7ae0\u7684\u5185\u5bb9\u662f MCMC, \u8fd9\u4e24\u79cd\u65b9\u6cd5\u662f\u8d1d\u53f6\u65af\u63a8\u65ad\u7684\u4e24\u6761\u6700\u4e3b\u8981\u8def\u7ebf \u5982\u679c\u5728\u8bfb PRML \u7684\u8fc7\u7a0b\u4e2d\u53d1\u73b0\u6709\u4efb\u4f55\u4e0d\u61c2\u7684\u540d\u8bcd\uff0c\u5c31\u53bb\u7ffb\u524d\u9762\u7684\u7ae0\u8282\u3002\u5f88\u5927\u6982\u7387\u80fd\u591f\u5728\u7b2c 3\uff0c4 \u7ae0\u627e\u5230\u76f8\u5bf9\u5e94\u7684\u5b9a\u4e49\uff1b\u5982\u679c\u627e\u4e0d\u5230\u6216\u8005\u4e0d\u591f\u8be6\u7ec6\uff0c\u5c31\u53bb\u67e5 MLAPP AoS \u7b2c 8 \u7ae0 (Parametric Inference) \u548c\u7b2c 11 \u7ae0 (Bayesian Inference) \u4e5f\u53ef\u4ee5\u4f5c\u4e3a\u53c2\u8003\u3002\u6700\u597d\u7684\u65b9\u6cd5\u662f\u591a\u672c\u4e66\u5bf9\u6bd4\u9605\u8bfb\uff0c\u6d41\u7a0b\u5982\u4e0b \u5047\u8bbe\u6211\u5728\u8bfb PRML \u7b2c 10 \u7ae0\u7684\u65f6\u5019\u53d1\u73b0\u4e86\u4e00\u4e2a\u4e0d\u61c2\u7684\u8bcd\uff1aposterior inference \u4e8e\u662f\u6211\u5f80\u524d\u7ffb\uff0c\u7ffb\u5230\u4e86\u7b2c 3 \u7ae0 (Linear Model for Regression)\uff0c\u770b\u5230\u4e86\u6700\u7b80\u5355\u7684 posterior \u7136\u540e\u6211\u63a5\u7740\u7ffb AoS\uff0c\u7ffb\u5230\u4e86\u7b2c 11 \u7ae0\uff0c\u4e5f\u6709\u5bf9 posterior \u7684\u63cf\u8ff0 \u7136\u540e\u6211\u5bf9\u6bd4 PRML \u7b2c 10 \u7ae0\uff0c\u7b2c 3 \u7ae0\uff0cAoS \u7b2c 11 \u7ae0\uff0c\u4e09\u5904\u4e0d\u540c\u5730\u65b9\u5bf9 posterior \u7684\u89e3\u8bfb\uff0c\u6bd4\u8f83\u5176\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u5b8c PRML \u7b2c 10 \u548c 11 \u7ae0\u4e4b\u540e\uff0c\u63a5\u7740\u8bfb AoS \u7b2c 24 \u7ae0 (Simulation Methods)\uff0c\u7136\u540e\u628a\u5b83\u548c PRML \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb -- \u8fd9\u4fe9\u90fd\u662f\u8bb2 MCMC \u5982\u679c\u5230\u6b64\u5904\u53d1\u73b0\u8fd8\u6709\u57fa\u7840\u6982\u5ff5\u8bfb\u4e0d\u61c2\uff0c\u5c31\u56de\u5230 PRML \u7b2c 3 \u7ae0\uff0c\u628a\u5b83\u548c AoS \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb Again\uff0c\u5bf9\u6bd4\u9605\u8bfb\u975e\u5e38\u91cd\u8981\uff0c\u4e00\u5b9a\u8981\u628a\u4e0d\u540c\u672c\u4e66\u7684\u7c7b\u4f3c\u5185\u5bb9\u540c\u65f6\u6446\u5728\u9762\u524d\u76f8\u4e92\u5bf9\u6bd4\uff0c\u8fd9\u6837\u53ef\u4ee5\u663e\u8457\u589e\u5f3a\u8bb0\u5fc6 \u7136\u540e\u8bfb PRML \u7b2c 13 \u7ae0\uff08\u8df3\u8fc7\u7b2c 12 \u7ae0\uff09\uff0c\u8fd9\u4e00\u7ae0\u53ef\u4ee5\u548c MLAPP \u7684\u7b2c 17, 18 \u7ae0\u5bf9\u6bd4\u9605\u8bfb MLAPP \u7b2c 17 \u7ae0\u662f PRML \u7b2c 13.2 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 HMM MLAPP \u7b2c 18 \u7ae0\u662f PRML \u7b2c 13.3 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 LDS \u8bfb\u5b8c PRML \u7b2c 13 \u7ae0\u4e4b\u540e\uff0c\u518d\u53bb\u8bfb PRML \u7b2c 8 \u7ae0 (Graphical Models) -- \u6b64\u65f6\u8fd9\u90e8\u5206\u5e94\u8be5\u4f1a\u8bfb\u5f97\u5f88\u8f7b\u677e \u4ee5\u4e0a\u7684\u5185\u5bb9\u53ef\u4ee5\u8fdb\u4e00\u6b65\u5bf9\u7167 CMU 10-708 PGM \u8bfe\u7a0b\u6750\u6599 \u5230\u76ee\u524d\u4e3a\u6b62\uff0c\u5e94\u8be5\u80fd\u591f\u638c\u63e1 \u6982\u7387\u6a21\u578b\u7684\u57fa\u7840\u5b9a\u4e49 \u7cbe\u51c6\u63a8\u65ad - Sum-Product \u8fd1\u4f3c\u63a8\u65ad - MCMC \u8fd1\u4f3c\u63a8\u65ad - VI \u7136\u540e\u5c31\u53ef\u4ee5\u53bb\u505a\u66f4\u8fdb\u9636\u7684\u5185\u5bb9","title":"\u8fdb\u9636\u8def\u7ebf\u56fe"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_1","text":"\u6b64\u8def\u7ebf\u56fe\u9002\u7528\u4e8e\u5df2\u7ecf\u5b66\u8fc7\u4e86\u57fa\u7840\u673a\u5668\u5b66\u4e60 (ML, NLP, CV, RL) \u7684\u540c\u5b66 (\u9ad8\u5e74\u7ea7\u672c\u79d1\u751f\u6216\u4f4e\u5e74\u7ea7\u7814\u7a76\u751f)\uff0c\u5df2\u7ecf\u53d1\u8868\u8fc7\u81f3\u5c11\u4e00\u7bc7\u9876\u4f1a\u8bba\u6587 (NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, CVPR, ICCV) \u60f3\u8981\u8d70\u673a\u5668\u5b66\u4e60\u79d1\u7814\u8def\u7ebf\u7684\u9009\u624b\u3002 \u6b64\u8def\u7ebf\u7684\u76ee\u6807\u662f\u4e3a\u8bfb\u61c2\u4e0e\u53d1\u8868\u673a\u5668\u5b66\u4e60\u9876\u4f1a\u8bba\u6587\u6253\u4e0b\u7406\u8bba\u57fa\u7840\uff0c\u7279\u522b\u662f Probabilistic Methods \u8fd9\u4e2a track \u4e0b\u7684\u6587\u7ae0 \u673a\u5668\u5b66\u4e60\u8fdb\u9636\u53ef\u80fd\u5b58\u5728\u591a\u79cd\u4e0d\u540c\u7684\u5b66\u4e60\u8def\u7ebf\uff0c\u6b64\u8def\u7ebf\u53ea\u80fd\u4ee3\u8868\u4f5c\u8005 Yao Fu \u6240\u7406\u89e3\u7684\u6700\u4f73\u8def\u5f84\uff0c\u4fa7\u91cd\u4e8e\u8d1d\u53f6\u65af\u5b66\u6d3e\u4e0b\u7684\u6982\u7387\u5efa\u6a21\u65b9\u6cd5\uff0c\u4e5f\u4f1a\u6d89\u53ca\u5230\u5404\u9879\u76f8\u5173\u5b66\u79d1\u7684\u4ea4\u53c9\u77e5\u8bc6\u3002","title":"\u673a\u5668\u5b66\u4e60\u8fdb\u9636"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_2","text":"PRML: Pattern Recognition and Machine Learning. Christopher Bishop \u7ecf\u5178\u8d1d\u53f6\u65af\u5b66\u6d3e\u6559\u6750 AoS: All of Statistics. Larry Wasserman \u7ecf\u5178\u9891\u7387\u5b66\u6d3e\u6559\u6750 \u6240\u4ee5\u8fd9\u4e24\u672c\u4e66\u521a\u597d\u76f8\u8f85\u76f8\u6210","title":"\u5fc5\u8bfb\u6559\u6750"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_3","text":"MLAPP: Machine Learning: A Probabilistic Perspective. Kevin Murphy Convex Optimization. Stephen Boyd and Lieven Vandenberghe","title":"\u5b57\u5178"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_4","text":"W&J: Graphical Models, Exponential Families, and Variational Inference. Martin Wainwright and Michael Jordan Theory of Point Estimation. E. L. Lehmann and George Casella","title":"\u8fdb\u9636\u4e66\u7c4d"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_5","text":"","title":"\u5982\u4f55\u9605\u8bfb"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#guidelines","text":"\u5fc5\u8bfb\u6559\u6750\u5c31\u662f\u4e00\u5b9a\u8981\u8bfb\u7684\u6559\u6750 \u5b57\u5178\u7684\u610f\u601d\u662f\uff0c\u4e00\u822c\u60c5\u51b5\u4e0b\u4e0d\u7ba1\u5b83\uff0c\u4f46\u5f53\u9047\u5230\u4e86\u4e0d\u61c2\u7684\u6982\u5ff5\u7684\u65f6\u5019\uff0c\u5c31\u53bb\u5b57\u5178\u91cc\u9762\u67e5\uff08\u800c\u4e0d\u662f\u7ef4\u57fa\u767e\u79d1\uff09 \u8fdb\u9636\u4e66\u7c4d\u5148\u4e0d\u8bfb\uff0c\u5148\u8bfb\u5b8c\u5fc5\u8bfb\u4e66\u7c4d\u3002\u5fc5\u8bfb\u4e66\u7c4d\u4e00\u822c\u90fd\u662f\u8981\u524d\u524d\u540e\u540e\u53cd\u590d\u770b\u8fc7 N \u904d\u624d\u7b97\u8bfb\u5b8c \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u6700\u91cd\u8981\u7684\u8bfb\u6cd5\u5c31\u662f\u5bf9\u6bd4\u9605\u8bfb (contrastive-comparative reading)\uff1a\u540c\u65f6\u6253\u5f00\u4e24\u672c\u4e66\u8bb2\u540c\u4e00\u4e3b\u9898\u7684\u7ae0\u8282\uff0c\u7136\u540e\u5bf9\u6bd4\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u5c3d\u91cf\u53bb\u56de\u60f3\u4e4b\u524d\u8bfb\u8fc7\u7684\u8bba\u6587\uff0c\u6bd4\u8f83\u8bba\u6587\u548c\u6559\u6750\u7684\u76f8\u540c\u70b9\u4e0e\u4e0d\u540c\u70b9","title":"Guidelines"},{"location":"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_6","text":"\u5148\u8bfb AoS \u7b2c\u516d\u7ae0: Models, Statistical Inference and Learning\uff0c\u8fd9\u4e00\u90e8\u5206\u662f\u6700\u57fa\u7840\u7684\u79d1\u666e \u7136\u540e\u8bfb PRML \u7b2c 10, 11 \u7ae0 \u7b2c 10 \u7ae0\u7684\u5185\u5bb9\u662f Variational Inference, \u7b2c 11 \u7ae0\u7684\u5185\u5bb9\u662f MCMC, \u8fd9\u4e24\u79cd\u65b9\u6cd5\u662f\u8d1d\u53f6\u65af\u63a8\u65ad\u7684\u4e24\u6761\u6700\u4e3b\u8981\u8def\u7ebf \u5982\u679c\u5728\u8bfb PRML \u7684\u8fc7\u7a0b\u4e2d\u53d1\u73b0\u6709\u4efb\u4f55\u4e0d\u61c2\u7684\u540d\u8bcd\uff0c\u5c31\u53bb\u7ffb\u524d\u9762\u7684\u7ae0\u8282\u3002\u5f88\u5927\u6982\u7387\u80fd\u591f\u5728\u7b2c 3\uff0c4 \u7ae0\u627e\u5230\u76f8\u5bf9\u5e94\u7684\u5b9a\u4e49\uff1b\u5982\u679c\u627e\u4e0d\u5230\u6216\u8005\u4e0d\u591f\u8be6\u7ec6\uff0c\u5c31\u53bb\u67e5 MLAPP AoS \u7b2c 8 \u7ae0 (Parametric Inference) \u548c\u7b2c 11 \u7ae0 (Bayesian Inference) \u4e5f\u53ef\u4ee5\u4f5c\u4e3a\u53c2\u8003\u3002\u6700\u597d\u7684\u65b9\u6cd5\u662f\u591a\u672c\u4e66\u5bf9\u6bd4\u9605\u8bfb\uff0c\u6d41\u7a0b\u5982\u4e0b \u5047\u8bbe\u6211\u5728\u8bfb PRML \u7b2c 10 \u7ae0\u7684\u65f6\u5019\u53d1\u73b0\u4e86\u4e00\u4e2a\u4e0d\u61c2\u7684\u8bcd\uff1aposterior inference \u4e8e\u662f\u6211\u5f80\u524d\u7ffb\uff0c\u7ffb\u5230\u4e86\u7b2c 3 \u7ae0 (Linear Model for Regression)\uff0c\u770b\u5230\u4e86\u6700\u7b80\u5355\u7684 posterior \u7136\u540e\u6211\u63a5\u7740\u7ffb AoS\uff0c\u7ffb\u5230\u4e86\u7b2c 11 \u7ae0\uff0c\u4e5f\u6709\u5bf9 posterior \u7684\u63cf\u8ff0 \u7136\u540e\u6211\u5bf9\u6bd4 PRML \u7b2c 10 \u7ae0\uff0c\u7b2c 3 \u7ae0\uff0cAoS \u7b2c 11 \u7ae0\uff0c\u4e09\u5904\u4e0d\u540c\u5730\u65b9\u5bf9 posterior \u7684\u89e3\u8bfb\uff0c\u6bd4\u8f83\u5176\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u5b8c PRML \u7b2c 10 \u548c 11 \u7ae0\u4e4b\u540e\uff0c\u63a5\u7740\u8bfb AoS \u7b2c 24 \u7ae0 (Simulation Methods)\uff0c\u7136\u540e\u628a\u5b83\u548c PRML \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb -- \u8fd9\u4fe9\u90fd\u662f\u8bb2 MCMC \u5982\u679c\u5230\u6b64\u5904\u53d1\u73b0\u8fd8\u6709\u57fa\u7840\u6982\u5ff5\u8bfb\u4e0d\u61c2\uff0c\u5c31\u56de\u5230 PRML \u7b2c 3 \u7ae0\uff0c\u628a\u5b83\u548c AoS \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb Again\uff0c\u5bf9\u6bd4\u9605\u8bfb\u975e\u5e38\u91cd\u8981\uff0c\u4e00\u5b9a\u8981\u628a\u4e0d\u540c\u672c\u4e66\u7684\u7c7b\u4f3c\u5185\u5bb9\u540c\u65f6\u6446\u5728\u9762\u524d\u76f8\u4e92\u5bf9\u6bd4\uff0c\u8fd9\u6837\u53ef\u4ee5\u663e\u8457\u589e\u5f3a\u8bb0\u5fc6 \u7136\u540e\u8bfb PRML \u7b2c 13 \u7ae0\uff08\u8df3\u8fc7\u7b2c 12 \u7ae0\uff09\uff0c\u8fd9\u4e00\u7ae0\u53ef\u4ee5\u548c MLAPP \u7684\u7b2c 17, 18 \u7ae0\u5bf9\u6bd4\u9605\u8bfb MLAPP \u7b2c 17 \u7ae0\u662f PRML \u7b2c 13.2 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 HMM MLAPP \u7b2c 18 \u7ae0\u662f PRML \u7b2c 13.3 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 LDS \u8bfb\u5b8c PRML \u7b2c 13 \u7ae0\u4e4b\u540e\uff0c\u518d\u53bb\u8bfb PRML \u7b2c 8 \u7ae0 (Graphical Models) -- \u6b64\u65f6\u8fd9\u90e8\u5206\u5e94\u8be5\u4f1a\u8bfb\u5f97\u5f88\u8f7b\u677e \u4ee5\u4e0a\u7684\u5185\u5bb9\u53ef\u4ee5\u8fdb\u4e00\u6b65\u5bf9\u7167 CMU 10-708 PGM \u8bfe\u7a0b\u6750\u6599 \u5230\u76ee\u524d\u4e3a\u6b62\uff0c\u5e94\u8be5\u80fd\u591f\u638c\u63e1 \u6982\u7387\u6a21\u578b\u7684\u57fa\u7840\u5b9a\u4e49 \u7cbe\u51c6\u63a8\u65ad - Sum-Product \u8fd1\u4f3c\u63a8\u65ad - MCMC \u8fd1\u4f3c\u63a8\u65ad - VI \u7136\u540e\u5c31\u53ef\u4ee5\u53bb\u505a\u66f4\u8fdb\u9636\u7684\u5185\u5bb9","title":"\u57fa\u7840\u8def\u5f84"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/","text":"CS224n: Natural Language Processing \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684 NLP \u5165\u95e8\u8bfe\u7a0b\uff0c\u7531\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9886\u57df\u7684\u5de8\u4f6c Chris Manning \u9886\u8854\u6559\u6388\uff08word2vec \u7b97\u6cd5\u7684\u5f00\u521b\u8005\uff09\u3002\u5185\u5bb9\u8986\u76d6\u4e86\u8bcd\u5411\u91cf\u3001RNN\u3001LSTM\u3001Seq2Seq \u6a21\u578b\u3001\u673a\u5668\u7ffb\u8bd1\u3001\u6ce8\u610f\u529b\u673a\u5236\u3001Transformer \u7b49\u7b49 NLP \u9886\u57df\u7684\u6838\u5fc3\u77e5\u8bc6\u70b9\u3002 5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\u96be\u5ea6\u5faa\u5e8f\u6e10\u8fdb\uff0c\u5206\u522b\u662f\u8bcd\u5411\u91cf\u3001word2vec \u7b97\u6cd5\u3001Dependency parsing\u3001\u673a\u5668\u7ffb\u8bd1\u4ee5\u53ca Transformer \u7684 fine-tune\u3002 \u6700\u7ec8\u7684\u5927\u4f5c\u4e1a\u662f\u5728 Stanford \u8457\u540d\u7684 SQuAD \u6570\u636e\u96c6\u4e0a\u8bad\u7ec3 QA \u6a21\u578b\uff0c\u6709\u5b66\u751f\u7684\u5927\u4f5c\u4e1a\u751a\u81f3\u76f4\u63a5\u53d1\u8868\u4e86\u9876\u4f1a\u8bba\u6587\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs224n/index.html \u8bfe\u7a0b\u89c6\u9891\uff1aB \u7ad9\u641c\u7d22 CS224n \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224n/index.html \uff0c5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a + 1 \u4e2a Final Project \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS224n - GitHub \u4e2d\u3002","title":"Stanford CS224n: Natural Language Processing"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#cs224n-natural-language-processing","text":"","title":"CS224n: Natural Language Processing"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684 NLP \u5165\u95e8\u8bfe\u7a0b\uff0c\u7531\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9886\u57df\u7684\u5de8\u4f6c Chris Manning \u9886\u8854\u6559\u6388\uff08word2vec \u7b97\u6cd5\u7684\u5f00\u521b\u8005\uff09\u3002\u5185\u5bb9\u8986\u76d6\u4e86\u8bcd\u5411\u91cf\u3001RNN\u3001LSTM\u3001Seq2Seq \u6a21\u578b\u3001\u673a\u5668\u7ffb\u8bd1\u3001\u6ce8\u610f\u529b\u673a\u5236\u3001Transformer \u7b49\u7b49 NLP \u9886\u57df\u7684\u6838\u5fc3\u77e5\u8bc6\u70b9\u3002 5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\u96be\u5ea6\u5faa\u5e8f\u6e10\u8fdb\uff0c\u5206\u522b\u662f\u8bcd\u5411\u91cf\u3001word2vec \u7b97\u6cd5\u3001Dependency parsing\u3001\u673a\u5668\u7ffb\u8bd1\u4ee5\u53ca Transformer \u7684 fine-tune\u3002 \u6700\u7ec8\u7684\u5927\u4f5c\u4e1a\u662f\u5728 Stanford \u8457\u540d\u7684 SQuAD \u6570\u636e\u96c6\u4e0a\u8bad\u7ec3 QA \u6a21\u578b\uff0c\u6709\u5b66\u751f\u7684\u5927\u4f5c\u4e1a\u751a\u81f3\u76f4\u63a5\u53d1\u8868\u4e86\u9876\u4f1a\u8bba\u6587\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs224n/index.html \u8bfe\u7a0b\u89c6\u9891\uff1aB \u7ad9\u641c\u7d22 CS224n \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224n/index.html \uff0c5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a + 1 \u4e2a Final Project","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS224n - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/","text":"CS224w: Machine Learning with Graphs \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython, LaTeX \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684\u56fe\u795e\u7ecf\u7f51\u7edc\u5165\u95e8\u8bfe\uff0c\u8fd9\u95e8\u8bfe\u6211\u6ca1\u6709\u4e0a\u8fc7\uff0c\u4f46\u4f17\u591a\u505a GNN \u7684\u670b\u53cb\u90fd\u5411\u6211\u529b\u8350\u8fc7\u8fd9\u95e8\u8bfe\uff0c\u60f3\u5fc5 Stanford \u7684\u8bfe\u8d28\u91cf\u8fd8\u662f\u4e00\u5982\u65e2\u5f80\u5730\u6709\u4fdd\u8bc1\u7684\u3002\u53e6\u5916\u5c31\u662f\u8fd9\u95e8\u8bfe\u7684\u6388\u8bfe\u8001\u5e08\u975e\u5e38\u5e74\u8f7b\u5e05\u6c14:) \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs224w/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/watch?v=JAB_plj2rbA \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224w/ \uff0c6 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\uff0c3 \u4e2a LaTeX \u4e66\u9762\u4f5c\u4e1a","title":"Stanford CS224w: Machine Learning with Graphs"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/#cs224w-machine-learning-with-graphs","text":"","title":"CS224w: Machine Learning with Graphs"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython, LaTeX \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684\u56fe\u795e\u7ecf\u7f51\u7edc\u5165\u95e8\u8bfe\uff0c\u8fd9\u95e8\u8bfe\u6211\u6ca1\u6709\u4e0a\u8fc7\uff0c\u4f46\u4f17\u591a\u505a GNN \u7684\u670b\u53cb\u90fd\u5411\u6211\u529b\u8350\u8fc7\u8fd9\u95e8\u8bfe\uff0c\u60f3\u5fc5 Stanford \u7684\u8bfe\u8d28\u91cf\u8fd8\u662f\u4e00\u5982\u65e2\u5f80\u5730\u6709\u4fdd\u8bc1\u7684\u3002\u53e6\u5916\u5c31\u662f\u8fd9\u95e8\u8bfe\u7684\u6388\u8bfe\u8001\u5e08\u975e\u5e38\u5e74\u8f7b\u5e05\u6c14:)","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs224w/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/watch?v=JAB_plj2rbA \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224w/ \uff0c6 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\uff0c3 \u4e2a LaTeX \u4e66\u9762\u4f5c\u4e1a","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS230/","text":"Coursera: Deep Learning \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u673a\u5668\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 \u5434\u6069\u8fbe\u5728 Coursera \u5f00\u8bbe\u7684\u53e6\u4e00\u95e8\u7f51\u7ea2\u8bfe\u7a0b\uff0c\u5b66\u4e60\u8005\u65e0\u6570\uff0c\u582a\u79f0\u5723\u7ecf\u7ea7\u7684\u6df1\u5ea6\u5b66\u4e60\u5165\u95e8\u8bfe\u3002\u6df1\u5165\u6d45\u51fa\u7684\u8bb2\u89e3\uff0c\u773c\u82b1\u7f2d\u4e71\u7684 Project\u3002\u4ece\u6700\u57fa\u7840\u7684\u795e\u7ecf\u7f51\u7edc\uff0c\u5230 CNN, RNN\uff0c\u518d\u5230\u6700\u8fd1\u5927\u70ed\u7684 Transformer\u3002\u5b66\u5b8c\u8fd9\u95e8\u8bfe\uff0c\u4f60\u5c06\u521d\u6b65\u638c\u63e1\u6df1\u5ea6\u5b66\u4e60\u9886\u57df\u5fc5\u5907\u7684\u77e5\u8bc6\u548c\u6280\u80fd\uff0c\u5e76\u4e14\u53ef\u4ee5\u5728 Kaggle \u4e2d\u53c2\u52a0\u81ea\u5df1\u611f\u5174\u8da3\u7684\u6bd4\u8d5b\uff0c\u5728\u5b9e\u8df5\u4e2d\u953b\u70bc\u81ea\u5df1\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.coursera.org/specializations/deep-learning \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.coursera.org/specializations/deep-learning \uff0cB\u7ad9\u6709\u642c\u8fd0 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://www.coursera.org/specializations/deep-learning","title":"Coursera: Deep Learning"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS230/#coursera-deep-learning","text":"","title":"Coursera: Deep Learning"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS230/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u673a\u5668\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 \u5434\u6069\u8fbe\u5728 Coursera 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https://www.coursera.org/specializations/deep-learning \uff0cB\u7ad9\u6709\u642c\u8fd0 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://www.coursera.org/specializations/deep-learning","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS231/","text":"CS231n: CNN for Visual Recognition \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u673a\u5668\u5b66\u4e60\u57fa\u7840 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684 CV \u5165\u95e8\u8bfe\uff0c\u7531\u8ba1\u7b97\u673a\u9886\u57df\u7684\u5de8\u4f6c\u674e\u98de\u98de\u9662\u58eb\u9886\u8854\u6559\u6388\uff08CV \u9886\u57df\u5212\u65f6\u4ee3\u7684\u8457\u540d\u6570\u636e\u96c6 ImageNet 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Regression\u3001Classification\u3001CNN\u3001Self-Attention\u3001Transformer\u3001GAN\u3001BERT\u3001Anomaly Detection\u3001Explainable AI\u3001Attack\u3001Adaptation\u3001 RL\u3001Compression\u3001Life-Long Learning \u4ee5\u53ca Meta Learning\u3002\u53ef\u8c13\u662f\u5305\u7f57\u4e07\u8c61\uff0c\u80fd\u8ba9\u5b66\u751f\u5bf9\u4e8e\u6df1\u5ea6\u5b66\u4e60\u7684\u7edd\u5927\u591a\u6570\u9886\u57df\u90fd\u6709\u4e00\u5b9a\u4e86\u89e3\uff0c\u4ece\u800c\u53ef\u4ee5\u8fdb\u4e00\u6b65\u9009\u62e9\u60f3\u8981\u6df1\u5165\u7684\u65b9\u5411\u8fdb\u884c\u5b66\u4e60\u3002 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Oppenheim \u597d\u7684\uff0c\u4e0a\u8fd9\u95e8\u8bfe\u7684\u7406\u7531\u5df2\u7ecf\u8db3\u591f\u4e86\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/Signals_and_Systems_AVO/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://ocw.mit.edu/resources/res-6-007-signals-and-systems-spring-2011/index.htm \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1CZ4y1j7hs \u8bfe\u7a0b\u6559\u6750\uff1aSignals and Systems, 2nd Edition \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://ocw.mit.edu/resources/res-6-007-signals-and-systems-spring-2011/assignments","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/signal/","text":"UCB EE120: Signal and Systems \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A, CS70\uff0c\u5fae\u79ef\u5206\uff0c\u7ebf\u6027\u4ee3\u6570 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u6700\u7cbe\u534e\u7684\u90e8\u5206\u5c31\u662f 6 \u4e2a\u8d85\u6709\u8da3\u7684\u7f16\u7a0b\u4f5c\u4e1a\u4e86\uff0c\u4f1a\u8ba9\u4f60\u7528 Python \u901a\u8fc7\u5b66\u4e60\u5230\u7684\u4fe1\u53f7\u4e0e\u7cfb\u7edf\u7684\u7406\u8bba\u77e5\u8bc6\uff0c\u89e3\u51b3\u5404\u7c7b\u5b9e\u9645\u95ee\u9898\u3002\u4f8b\u5982 lab3 \u4f1a\u8ba9\u4f60\u5b9e\u73b0 FFT \u7b97\u6cd5\uff0c\u5e76\u548c Numpy \u7684\u5b98\u65b9\u5b9e\u73b0\u8fdb\u884c\u6027\u80fd\u5bf9\u6bd4\uff1blab4 \u4f1a\u901a\u8fc7\u5206\u6790\u624b\u6307\u5934\u7684\u5f71\u50cf\u6570\u636e\u63a8\u65ad\u5fc3\u7387\uff1blab5 \u5c31\u66f4\u725b\u4e86\uff0c\u4f1a\u8ba9\u4f60\u7ed9\u54c8\u52c3\u671b\u8fdc\u955c\u62cd\u5230\u7684\u7167\u7247\u8fdb\u884c\u964d\u566a\u5904\u7406\uff0c\u6062\u590d\u7eda\u70c2\u6e05\u6670\u7684\u661f\u7a7a\uff1blab6 \u4f1a\u8ba9\u4f60\u6784\u9020\u4e00\u4e2a\u53cd\u9988\u7cfb\u7edf\uff0c\u5e73\u8861\u5c0f\u8f66\u4e0a\u7684\u7ec6\u6746\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://inst.eecs.berkeley.edu/~ee120/fa19/ \u8bfe\u7a0b\u6559\u6750\uff1a\u53c2\u89c1\u8bfe\u7a0b notes \u8bfe\u7a0b\u4f5c\u4e1a\uff1a5 \u4e2a\u4e66\u9762\u4f5c\u4e1a + 6 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/UCB-EE120 - 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GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"%E7%A8%8B%E5%BA%8F%E8%AF%AD%E8%A8%80%E8%AE%BE%E8%AE%A1/CS242/","text":"","title":"CS242"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/","text":"UCB CS161: Computer Security \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A, CS61B, CS61C \u7f16\u7a0b\u8bed\u8a00\uff1aC, Go \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 \u4f2f\u514b\u5229\u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u8bfe\u7a0b\u5185\u5bb9\u5206\u4e3a5\u4e2a\u90e8\u5206\uff1a Security principles: how to design a secure system Memory safety: buffer overflow attack Cryptography: symmetric encryption, asymmetric encryption, MAC, digital signature ......... Web: SQL-injection, XSS, XSRF ....... Networking: attacks for each layer \u8fd9\u95e8\u8bfe\u8ba9\u6211\u5370\u8c61\u6700\u4e3a\u6df1\u523b\u7684\u90e8\u5206\u662f Project2\uff0c\u8ba9\u4f60\u7528 Go \u8bed\u8a00\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7684\u6587\u4ef6\u5206\u4eab\u7cfb\u7edf\u3002\u6211\u82b1\u4e86\u6574\u6574\u4e09\u5929\u624d\u5b8c\u6210\u4e86\u8fd9\u4e2a\u975e\u5e38\u8650\u7684 Project\uff0c\u603b\u4ee3\u7801\u91cf\u8d85\u8fc7 3k \u884c\u3002\u5728\u8fd9\u6837\u5bc6\u96c6\u578b\u7684\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u80fd\u6781\u5927\u5730\u953b\u70bc\u4f60\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7cfb\u7edf\u7684\u80fd\u529b\u3002 2020 \u5e74\u590f\u5b63\u5b66\u671f\u7684\u7248\u672c\u5f00\u6e90\u4e86\u8bfe\u7a0b\u5f55\u5f71\uff0c\u5927\u5bb6\u53ef\u4ee5\u5728\u4e0b\u9762\u7684\u8bfe\u7a0b\u7f51\u7ad9\u94fe\u63a5\u91cc\u627e\u5230\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://su20.cs161.org/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a https://textbook.cs161.org/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a7 \u4e2a\u5728\u7ebf HW + 3 \u4e2a Lab + 3 \u4e2a Project \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/UCB-CS161 - GitHub \u4e2d\u3002","title":"UCB CS161: Computer Security"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#ucb-cs161-computer-security","text":"","title":"UCB CS161: Computer Security"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A, CS61B, CS61C \u7f16\u7a0b\u8bed\u8a00\uff1aC, Go \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 \u4f2f\u514b\u5229\u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u8bfe\u7a0b\u5185\u5bb9\u5206\u4e3a5\u4e2a\u90e8\u5206\uff1a Security principles: how to design a secure system Memory safety: buffer overflow attack Cryptography: symmetric encryption, asymmetric encryption, MAC, digital signature ......... Web: SQL-injection, XSS, XSRF ....... Networking: attacks for each layer \u8fd9\u95e8\u8bfe\u8ba9\u6211\u5370\u8c61\u6700\u4e3a\u6df1\u523b\u7684\u90e8\u5206\u662f Project2\uff0c\u8ba9\u4f60\u7528 Go \u8bed\u8a00\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7684\u6587\u4ef6\u5206\u4eab\u7cfb\u7edf\u3002\u6211\u82b1\u4e86\u6574\u6574\u4e09\u5929\u624d\u5b8c\u6210\u4e86\u8fd9\u4e2a\u975e\u5e38\u8650\u7684 Project\uff0c\u603b\u4ee3\u7801\u91cf\u8d85\u8fc7 3k \u884c\u3002\u5728\u8fd9\u6837\u5bc6\u96c6\u578b\u7684\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u80fd\u6781\u5927\u5730\u953b\u70bc\u4f60\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7cfb\u7edf\u7684\u80fd\u529b\u3002 2020 \u5e74\u590f\u5b63\u5b66\u671f\u7684\u7248\u672c\u5f00\u6e90\u4e86\u8bfe\u7a0b\u5f55\u5f71\uff0c\u5927\u5bb6\u53ef\u4ee5\u5728\u4e0b\u9762\u7684\u8bfe\u7a0b\u7f51\u7ad9\u94fe\u63a5\u91cc\u627e\u5230\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://su20.cs161.org/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a https://textbook.cs161.org/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a7 \u4e2a\u5728\u7ebf HW + 3 \u4e2a Lab + 3 \u4e2a Project","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/UCB-CS161 - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/","text":"MIT 6.858: Computer System Security \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\uff0c\u5bf9\u8ba1\u7b97\u673a\u7cfb\u7edf\u6709\u521d\u6b65\u4e86\u89e3 \u7f16\u7a0b\u8bed\u8a00\uff1aC, Python \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 MIT \u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u5b9e\u9a8c\u73af\u5883\u662f\u4e00\u4e2a Web Application Zoobar\u3002\u5b66\u751f\u5b66\u4e60\u653b\u9632\u6280\u672f\u5e76\u5e94\u7528\u4e8e\u8be5 Web Application\u3002 Lab 1: you will explore the zoobar web application, and use buffer overflow attacks to break its security properties. Lab 2: you will improve the zoobar web application by using privilege separation, so that if one component is compromised, the adversary doesn't get control over the whole web application. Lab 3: you will build a program analysis tool based on symbolic execution to find bugs in Python code such as the zoobar web application. Lab 4: you will improve the zoobar application against browser attacks. \u8fd9\u4e2a\u8bfe\u6211\u4e3b\u8981\u662f\u505a\u4e86 Lab 3\u3002Lab 3 \u662f\u901a\u8fc7\u6df7\u5408\u7b26\u53f7\u6267\u884c\u6765\u904d\u5386\u7a0b\u5e8f\u7684\u6240\u6709\u5206\u652f\uff0c\u7406\u89e3\u4e86\u7b26\u53f7\u6267\u884c\u7684\u601d\u60f3\u540e Lab \u5e76\u4e0d\u96be\u505a\u3002\u8fd9\u4e2a Lab \u76f4\u89c2\u5c55\u793a\u7b26\u53f7\u6267\u884c\u8fd9\u79cd\u6280\u672f\u7684\u4f7f\u7528\u65b9\u6cd5\u3002 \u8fd9\u4e2a\u8bfe\u7684 Final Project \u662f\u5b9e\u73b0 SecFS \uff0c\u4e00\u4e2a\u8fdc\u7aef\u6587\u4ef6\u7cfb\u7edf\uff0c\u9762\u5bf9\u5b8c\u5168\u4e0d\u53ef\u4fe1\u7684\u670d\u52a1\u5668\uff0c\u63d0\u4f9b\u673a\u5bc6\u6027\u548c\u5b8c\u6574\u6027\u3002\u53c2\u8003\u8bba\u6587\u4e3a SUNDR \u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://css.csail.mit.edu/6.858/2022/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a4 \u4e2a Lab + Final Project / Lab5","title":"MIT 6.858: Computer System Security"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/#mit-6858-computer-system-security","text":"","title":"MIT 6.858: Computer System Security"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\uff0c\u5bf9\u8ba1\u7b97\u673a\u7cfb\u7edf\u6709\u521d\u6b65\u4e86\u89e3 \u7f16\u7a0b\u8bed\u8a00\uff1aC, Python \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 MIT \u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u5b9e\u9a8c\u73af\u5883\u662f\u4e00\u4e2a Web Application Zoobar\u3002\u5b66\u751f\u5b66\u4e60\u653b\u9632\u6280\u672f\u5e76\u5e94\u7528\u4e8e\u8be5 Web Application\u3002 Lab 1: you will explore the zoobar web application, and use buffer overflow attacks to break its security properties. Lab 2: you will improve the zoobar web application by using privilege separation, so that if one component is compromised, the adversary doesn't get control over the whole web application. Lab 3: you will build a program analysis tool based on symbolic execution to find bugs in Python code such as the zoobar web application. Lab 4: you will improve the zoobar application against browser attacks. \u8fd9\u4e2a\u8bfe\u6211\u4e3b\u8981\u662f\u505a\u4e86 Lab 3\u3002Lab 3 \u662f\u901a\u8fc7\u6df7\u5408\u7b26\u53f7\u6267\u884c\u6765\u904d\u5386\u7a0b\u5e8f\u7684\u6240\u6709\u5206\u652f\uff0c\u7406\u89e3\u4e86\u7b26\u53f7\u6267\u884c\u7684\u601d\u60f3\u540e Lab \u5e76\u4e0d\u96be\u505a\u3002\u8fd9\u4e2a Lab \u76f4\u89c2\u5c55\u793a\u7b26\u53f7\u6267\u884c\u8fd9\u79cd\u6280\u672f\u7684\u4f7f\u7528\u65b9\u6cd5\u3002 \u8fd9\u4e2a\u8bfe\u7684 Final Project \u662f\u5b9e\u73b0 SecFS \uff0c\u4e00\u4e2a\u8fdc\u7aef\u6587\u4ef6\u7cfb\u7edf\uff0c\u9762\u5bf9\u5b8c\u5168\u4e0d\u53ef\u4fe1\u7684\u670d\u52a1\u5668\uff0c\u63d0\u4f9b\u673a\u5bc6\u6027\u548c\u5b8c\u6574\u6027\u3002\u53c2\u8003\u8bba\u6587\u4e3a SUNDR \u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://css.csail.mit.edu/6.858/2022/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a4 \u4e2a Lab + Final Project / Lab5","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/","text":"Stanford CS106B/X: Programming Abstractions in C++ \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u57fa\u7840 (CS50/CS106A/CS61A or equivalent) \u7f16\u7a0b\u8bed\u8a00\uff1aC++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a50-70 \u5c0f\u65f6 Stanford \u7684\u8fdb\u9636\u7f16\u7a0b\u8bfe\uff0cCS106X \u5728\u96be\u5ea6\u548c\u6df1\u5ea6\u4e0a\u4f1a\u6bd4 CS106B \u6709\u6240\u63d0\u9ad8\uff0c\u4f46\u4e3b\u4f53\u5185\u5bb9\u7c7b\u4f3c\u3002\u4e3b\u8981\u901a\u8fc7 C++ \u8bed\u8a00\u8ba9\u5b66\u751f\u5728\u5b9e\u9645\u7684\u7f16\u7a0b\u4f5c\u4e1a\u91cc\u57f9\u517b\u901a\u8fc7\u7f16\u7a0b\u62bd\u8c61\u89e3\u51b3\u5b9e\u9645\u95ee\u9898\u7684\u80fd\u529b\uff0c\u540c\u65f6\u4e5f\u4f1a\u6d89\u53ca\u4e00\u4e9b\u7b80\u5355\u7684\u6570\u636e\u7ed3\u6784\u548c\u7b97\u6cd5\u7684\u77e5\u8bc6\uff0c\u4f46\u603b\u4f53\u6765\u8bf4\u6ca1\u6709\u4e00\u95e8\u4e13\u95e8\u7684\u6570\u636e\u7ed3\u6784\u8bfe\u90a3\u4e48\u7cfb\u7edf\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a CS106B , CS106X \u8bfe\u7a0b\u6559\u6750\uff1a https://web.stanford.edu/class/cs106x/res/reader/CS106BX-Reader.pdf \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1G7411k7jG","title":"Stanford CS106B/X"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/#stanford-cs106bx-programming-abstractions-in-c","text":"","title":"Stanford CS106B/X: Programming Abstractions in C++"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u57fa\u7840 (CS50/CS106A/CS61A or equivalent) \u7f16\u7a0b\u8bed\u8a00\uff1aC++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a50-70 \u5c0f\u65f6 Stanford \u7684\u8fdb\u9636\u7f16\u7a0b\u8bfe\uff0cCS106X \u5728\u96be\u5ea6\u548c\u6df1\u5ea6\u4e0a\u4f1a\u6bd4 CS106B \u6709\u6240\u63d0\u9ad8\uff0c\u4f46\u4e3b\u4f53\u5185\u5bb9\u7c7b\u4f3c\u3002\u4e3b\u8981\u901a\u8fc7 C++ \u8bed\u8a00\u8ba9\u5b66\u751f\u5728\u5b9e\u9645\u7684\u7f16\u7a0b\u4f5c\u4e1a\u91cc\u57f9\u517b\u901a\u8fc7\u7f16\u7a0b\u62bd\u8c61\u89e3\u51b3\u5b9e\u9645\u95ee\u9898\u7684\u80fd\u529b\uff0c\u540c\u65f6\u4e5f\u4f1a\u6d89\u53ca\u4e00\u4e9b\u7b80\u5355\u7684\u6570\u636e\u7ed3\u6784\u548c\u7b97\u6cd5\u7684\u77e5\u8bc6\uff0c\u4f46\u603b\u4f53\u6765\u8bf4\u6ca1\u6709\u4e00\u95e8\u4e13\u95e8\u7684\u6570\u636e\u7ed3\u6784\u8bfe\u90a3\u4e48\u7cfb\u7edf\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a CS106B , CS106X \u8bfe\u7a0b\u6559\u6750\uff1a https://web.stanford.edu/class/cs106x/res/reader/CS106BX-Reader.pdf \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1G7411k7jG","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/","text":"CS106L: Standard C++ Programming \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6700\u597d\u638c\u63e1\u81f3\u5c11\u4e00\u95e8\u7f16\u7a0b\u8bed\u8a00 \u7f16\u7a0b\u8bed\u8a00\uff1aC++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a20 \u5c0f\u65f6 \u6211\u4ece\u5927\u4e00\u5f00\u59cb\u4e00\u76f4\u90fd\u662f\u5199\u7684 C++ \u4ee3\u7801\uff0c\u76f4\u5230\u5b66\u5b8c\u8fd9\u95e8\u8bfe\u6211\u624d\u610f\u8bc6\u5230\uff0c\u6211\u5199\u7684 C++ \u4ee3\u7801\u5927\u6982\u53ea\u662f C \u8bed\u8a00 + cin / cout \u800c\u5df2\u3002 \u8fd9\u95e8\u8bfe\u4f1a\u6df1\u5165\u5230\u5f88\u591a\u6807\u51c6 C++ \u7684\u7279\u6027\u548c\u8bed\u6cd5\uff0c\u8ba9\u4f60\u7f16\u5199\u51fa\u9ad8\u8d28\u91cf\u7684 C++ \u4ee3\u7801\u3002\u4f8b\u5982 auto binding, uniform initialization, lambda function, move semantics\uff0cRAII \u7b49\u6280\u5de7\u90fd\u5728\u6211\u6b64\u540e\u7684\u4ee3\u7801\u751f\u6daf\u4e2d\u88ab\u53cd\u590d\u7528\u5230\uff0c\u975e\u5e38\u5b9e\u7528\u3002 \u503c\u5f97\u4e00\u63d0\u7684\u662f\uff0c\u8fd9\u95e8\u8bfe\u7684\u4f5c\u4e1a\u91cc\u4f60\u4f1a\u5b9e\u73b0\u4e00\u4e2a HashMap\uff08\u7c7b\u4f3c\u4e8e STL \u4e2d\u7684 unordered_map ), \u8fd9\u4e2a\u4f5c\u4e1a\u51e0\u4e4e\u628a\u6574\u4e2a\u8bfe\u7a0b\u4e32\u8054\u4e86\u8d77\u6765\uff0c\u975e\u5e38\u8003\u9a8c\u4ee3\u7801\u80fd\u529b\u3002\u7279\u522b\u662f iterator \u7684\u5b9e\u73b0\uff0c\u505a\u5b8c\u8fd9\u4e2a\u4f5c\u4e1a\u6211\u5f00\u59cb\u7406\u89e3\u4e3a\u4ec0\u4e48 Linus \u5bf9 C/C++ \u55e4\u4e4b\u4ee5\u9f3b\u4e86\uff0c\u56e0\u4e3a\u771f\u7684\u5f88\u96be\u5199\u5bf9\u3002 \u603b\u7684\u6765\u8bb2\u8fd9\u95e8\u8bfe\u5e76\u4e0d\u96be\uff0c\u4f46\u662f\u4fe1\u606f\u91cf\u5f88\u5927\uff0c\u9700\u8981\u4f60\u5728\u4e4b\u540e\u7684\u5f00\u53d1\u5b9e\u8df5\u4e2d\u53cd\u590d\u5de9\u56fa\u3002Stanford \u4e4b\u6240\u4ee5\u5355\u5f00\u4e00\u95e8 C++ \u7684\u7f16\u7a0b\u8bfe\uff0c\u662f\u56e0\u4e3a\u5b83\u540e\u7eed\u7684\u5f88\u591a CS \u8bfe\u7a0b Project \u90fd\u662f\u57fa\u4e8e C++\u7684\u3002\u4f8b\u5982 CS144 \u8ba1\u7b97\u673a\u7f51\u7edc\u548c CS143 \u7f16\u8bd1\u5668\u3002\u8fd9\u4e24\u95e8\u8bfe\u5728\u672c\u4e66\u4e2d\u5747\u6709\u6536\u5f55\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs106l/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/channel/UCSqr6y-eaQT_qZJVUm_4QxQ/playlists \u8bfe\u7a0b\u6559\u6750\uff1a http://web.stanford.edu/class/cs106l/full_course_reader.pdf Assignment1\u4e0b\u8f7d\u7f51\u5740\uff1a https://github.com/snme/cs106L-assignment1 Assignment2\u4e0b\u8f7d\u7f51\u5740\uff1a https://github.com/snme/cs106L-assignment2 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u5177\u4f53\u5185\u5bb9\u89c1\u8bfe\u7a0b\u7f51\u7ad9\uff0c\u6211\u505a\u7684\u65f6\u5019\u4e00\u5171\u662f\u4e24\u4e2a\uff1a \u5b9e\u73b0\u4e00\u4e2a WikiRacer \u7684\u5c0f\u6e38\u620f \u5b9e\u73b0\u4e00\u4e2a\u7c7b\u4f3c STL \u5e93\u7684 HashMap \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS106L - GitHub \u4e2d\u3002","title":"Stanford CS106L: Standard C++ Programming"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#cs106l-standard-c-programming","text":"","title":"CS106L: Standard C++ Programming"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6700\u597d\u638c\u63e1\u81f3\u5c11\u4e00\u95e8\u7f16\u7a0b\u8bed\u8a00 \u7f16\u7a0b\u8bed\u8a00\uff1aC++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a20 \u5c0f\u65f6 \u6211\u4ece\u5927\u4e00\u5f00\u59cb\u4e00\u76f4\u90fd\u662f\u5199\u7684 C++ \u4ee3\u7801\uff0c\u76f4\u5230\u5b66\u5b8c\u8fd9\u95e8\u8bfe\u6211\u624d\u610f\u8bc6\u5230\uff0c\u6211\u5199\u7684 C++ \u4ee3\u7801\u5927\u6982\u53ea\u662f C \u8bed\u8a00 + cin / cout \u800c\u5df2\u3002 \u8fd9\u95e8\u8bfe\u4f1a\u6df1\u5165\u5230\u5f88\u591a\u6807\u51c6 C++ \u7684\u7279\u6027\u548c\u8bed\u6cd5\uff0c\u8ba9\u4f60\u7f16\u5199\u51fa\u9ad8\u8d28\u91cf\u7684 C++ \u4ee3\u7801\u3002\u4f8b\u5982 auto binding, uniform initialization, lambda function, move semantics\uff0cRAII \u7b49\u6280\u5de7\u90fd\u5728\u6211\u6b64\u540e\u7684\u4ee3\u7801\u751f\u6daf\u4e2d\u88ab\u53cd\u590d\u7528\u5230\uff0c\u975e\u5e38\u5b9e\u7528\u3002 \u503c\u5f97\u4e00\u63d0\u7684\u662f\uff0c\u8fd9\u95e8\u8bfe\u7684\u4f5c\u4e1a\u91cc\u4f60\u4f1a\u5b9e\u73b0\u4e00\u4e2a HashMap\uff08\u7c7b\u4f3c\u4e8e STL \u4e2d\u7684 unordered_map ), \u8fd9\u4e2a\u4f5c\u4e1a\u51e0\u4e4e\u628a\u6574\u4e2a\u8bfe\u7a0b\u4e32\u8054\u4e86\u8d77\u6765\uff0c\u975e\u5e38\u8003\u9a8c\u4ee3\u7801\u80fd\u529b\u3002\u7279\u522b\u662f iterator \u7684\u5b9e\u73b0\uff0c\u505a\u5b8c\u8fd9\u4e2a\u4f5c\u4e1a\u6211\u5f00\u59cb\u7406\u89e3\u4e3a\u4ec0\u4e48 Linus \u5bf9 C/C++ \u55e4\u4e4b\u4ee5\u9f3b\u4e86\uff0c\u56e0\u4e3a\u771f\u7684\u5f88\u96be\u5199\u5bf9\u3002 \u603b\u7684\u6765\u8bb2\u8fd9\u95e8\u8bfe\u5e76\u4e0d\u96be\uff0c\u4f46\u662f\u4fe1\u606f\u91cf\u5f88\u5927\uff0c\u9700\u8981\u4f60\u5728\u4e4b\u540e\u7684\u5f00\u53d1\u5b9e\u8df5\u4e2d\u53cd\u590d\u5de9\u56fa\u3002Stanford \u4e4b\u6240\u4ee5\u5355\u5f00\u4e00\u95e8 C++ \u7684\u7f16\u7a0b\u8bfe\uff0c\u662f\u56e0\u4e3a\u5b83\u540e\u7eed\u7684\u5f88\u591a CS \u8bfe\u7a0b Project \u90fd\u662f\u57fa\u4e8e C++\u7684\u3002\u4f8b\u5982 CS144 \u8ba1\u7b97\u673a\u7f51\u7edc\u548c CS143 \u7f16\u8bd1\u5668\u3002\u8fd9\u4e24\u95e8\u8bfe\u5728\u672c\u4e66\u4e2d\u5747\u6709\u6536\u5f55\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs106l/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/channel/UCSqr6y-eaQT_qZJVUm_4QxQ/playlists \u8bfe\u7a0b\u6559\u6750\uff1a http://web.stanford.edu/class/cs106l/full_course_reader.pdf Assignment1\u4e0b\u8f7d\u7f51\u5740\uff1a https://github.com/snme/cs106L-assignment1 Assignment2\u4e0b\u8f7d\u7f51\u5740\uff1a https://github.com/snme/cs106L-assignment2 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u5177\u4f53\u5185\u5bb9\u89c1\u8bfe\u7a0b\u7f51\u7ad9\uff0c\u6211\u505a\u7684\u65f6\u5019\u4e00\u5171\u662f\u4e24\u4e2a\uff1a \u5b9e\u73b0\u4e00\u4e2a WikiRacer \u7684\u5c0f\u6e38\u620f \u5b9e\u73b0\u4e00\u4e2a\u7c7b\u4f3c STL \u5e93\u7684 HashMap","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS106L - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS110L/","text":"CS110L: Safety in Systems Programming \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6700\u597d\u6709\u4e00\u5b9a\u7684\u7f16\u7a0b\u80cc\u666f\u5e76\u5bf9\u8ba1\u7b97\u673a\u7cfb\u7edf\u6709\u521d\u6b65\u7684\u8ba4\u8bc6\u3002 \u7f16\u7a0b\u8bed\u8a00\uff1aRust \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a30 \u5c0f\u65f6 \u5728\u8fd9\u95e8\u8bfe\u4e2d\u4f60\u5c06\u4f1a\u5b66\u4e60 Rust \u8fd9\u95e8\u795e\u5947\u7684\u8bed\u8a00\u3002 \u5982\u679c\u4f60\u5b66\u8fc7 C \u5e76\u63a5\u89e6\u8fc7\u4e00\u4e9b\u7cfb\u7edf\u7f16\u7a0b\u7684\u8bdd\uff0c\u5e94\u8be5\u5bf9 C \u7684\u5185\u5b58\u6cc4\u6f0f\u4ee5\u53ca\u6307\u9488\u7684\u5371\u9669\u6709\u6240\u8033\u95fb\uff0c\u4f46 C \u7684\u5e95\u5c42\u7279\u6027\u4ee5\u53ca\u9ad8\u6548\u4ecd\u7136\u8ba9\u5b83\u5728\u7cfb\u7edf\u7ea7\u7f16\u7a0b\u4e2d\u65e0\u6cd5\u88ab\u4f8b\u5982 Java \u7b49\u81ea\u5e26\u5783\u573e\u6536\u96c6\u673a\u5236\u7684\u9ad8\u7ea7\u8bed\u8a00\u6240\u66ff\u4ee3\u3002\u800c Rust \u7684\u76ee\u6807\u5219\u662f\u5e0c\u671b\u5728 C \u7684\u9ad8\u6548\u57fa\u7840\u4e0a\uff0c\u5f25\u8865\u5176\u5b89\u5168\u4e0d\u8db3\u7684\u7f3a\u70b9\u3002\u56e0\u6b64 Rust \u5728\u8bbe\u8ba1\u4e4b\u521d\uff0c\u5c31\u6709\u5e26\u6709\u5f88\u591a\u7cfb\u7edf\u7f16\u7a0b\u7684\u89c2\u70b9\u3002\u5b66\u4e60 Rust\uff0c\u4e5f\u80fd\u8ba9\u4f60\u4e4b\u540e\u80fd\u7528 C \u8bed\u8a00\u7f16\u5199\u51fa\u66f4\u5b89\u5168\u66f4\u4f18\u96c5\u7684\u7cfb\u7edf\u7ea7\u4ee3\u7801\uff08\u4f8b\u5982\u64cd\u4f5c\u7cfb\u7edf\u7b49\uff09\u3002 \u8fd9\u95e8\u8bfe\u7684\u540e\u534a\u90e8\u5206\u5173\u6ce8\u5728\u5e76\u53d1\uff08concurrency\uff09\u8fd9\u4e00\u4e3b\u9898\u4e0a\uff0c\u4f60\u5c06\u4f1a\u7cfb\u7edf\u5730\u638c\u63e1\u591a\u8fdb\u7a0b\u3001\u591a\u7ebf\u7a0b\u3001\u57fa\u4e8e\u4e8b\u4ef6\u9a71\u52a8\u7684\u5e76\u53d1\u7b49\u82e5\u5e72\u5e76\u53d1\u6280\u672f\uff0c\u5e76\u5728\u7b2c\u4e8c\u4e2a Project \u4e2d\u6bd4\u8f83\u5b83\u4eec\u5404\u81ea\u7684\u4f18\u52a3\u3002Rust \u4e2d \u201cfutures\u201d \u7684\u6982\u5ff5\u975e\u5e38\u6709\u8da3\u548c\u4f18\u96c5\uff0c\u8fd9\u4e9b\u57fa\u7840\u77e5\u8bc6\u5bf9\u4f60\u540e\u7eed\u5bf9\u8ba1\u7b97\u673a\u7cfb\u7edf\u76f8\u5173\u8bfe\u7a0b\u7684\u5b66\u4e60\u5f88\u6709\u5e2e\u52a9\u3002\u53e6\u5916\uff0c\u6e05\u534e\u5927\u5b66\u7684\u64cd\u7edf\u5b9e\u9a8c rCore \u5c31\u662f\u57fa\u4e8e Rust \u7f16\u5199\u7684\uff0c\u5177\u4f53\u53c2\u89c1 \u6587\u6863 \u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://reberhardt.com/cs110l/spring-2020/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://youtu.be/j7AQrtLevUE \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u5171 6 \u4e2a Lab \u548c 2 \u4e2a Project\uff0c\u4f5c\u4e1a\u6587\u6863\u548c\u4ee3\u7801\u6846\u67b6\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9\u3002\u5176\u4e2d\u4e24\u4e2a Project \u975e\u5e38\u6709\u8da3\uff0c\u5206\u522b\u662f\uff1a \u7528 Rust \u5b9e\u73b0\u4e00\u4e2a\u7c7b\u4f3c\u4e8e GDB \u7684 debugger \u7528 Rust 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\u8fd9\u95e8\u795e\u5947\u7684\u8bed\u8a00\u3002 \u5982\u679c\u4f60\u5b66\u8fc7 C \u5e76\u63a5\u89e6\u8fc7\u4e00\u4e9b\u7cfb\u7edf\u7f16\u7a0b\u7684\u8bdd\uff0c\u5e94\u8be5\u5bf9 C \u7684\u5185\u5b58\u6cc4\u6f0f\u4ee5\u53ca\u6307\u9488\u7684\u5371\u9669\u6709\u6240\u8033\u95fb\uff0c\u4f46 C \u7684\u5e95\u5c42\u7279\u6027\u4ee5\u53ca\u9ad8\u6548\u4ecd\u7136\u8ba9\u5b83\u5728\u7cfb\u7edf\u7ea7\u7f16\u7a0b\u4e2d\u65e0\u6cd5\u88ab\u4f8b\u5982 Java \u7b49\u81ea\u5e26\u5783\u573e\u6536\u96c6\u673a\u5236\u7684\u9ad8\u7ea7\u8bed\u8a00\u6240\u66ff\u4ee3\u3002\u800c Rust \u7684\u76ee\u6807\u5219\u662f\u5e0c\u671b\u5728 C \u7684\u9ad8\u6548\u57fa\u7840\u4e0a\uff0c\u5f25\u8865\u5176\u5b89\u5168\u4e0d\u8db3\u7684\u7f3a\u70b9\u3002\u56e0\u6b64 Rust \u5728\u8bbe\u8ba1\u4e4b\u521d\uff0c\u5c31\u6709\u5e26\u6709\u5f88\u591a\u7cfb\u7edf\u7f16\u7a0b\u7684\u89c2\u70b9\u3002\u5b66\u4e60 Rust\uff0c\u4e5f\u80fd\u8ba9\u4f60\u4e4b\u540e\u80fd\u7528 C 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\u7f16\u7a0b\u8bed\u8a00\uff1aC++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a60 \u5c0f\u65f6 \u5b98\u65b9\u4ecb\u7ecd: \u672c\u8bfe\u7a0b\u5c06\u5168\u9762\u5730\u4ecb\u7ecd\u73b0\u4ee3\u5b9e\u65f6\u6e32\u67d3\u4e2d\u7684\u5173\u952e\u95ee\u9898\u4e0e\u89e3\u51b3\u65b9\u6cd5\u3002\u7531\u4e8e\u5b9e\u65f6\u6e32\u67d3 (>30 FPS) \u5bf9\u901f\u5ea6\u8981\u6c42\u6781\u9ad8\uff0c\u56e0\u6b64\u672c\u8bfe\u7a0b\u7684\u5173\u6ce8\u70b9\u5c06\u662f\u5728\u82db\u523b\u7684\u65f6\u95f4\u9650\u5236\u4e0b\uff0c\u4eba\u4eec\u5982\u4f55\u6253\u7834\u901f\u5ea6\u4e0e\u8d28\u91cf\u4e4b\u95f4\u7684\u6743\u8861\uff0c\u540c\u65f6\u4fdd\u8bc1\u5b9e\u65f6\u7684\u9ad8\u901f\u5ea6\u4e0e\u7167\u7247\u7ea7\u7684\u771f\u5b9e\u611f\u3002 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Approach"},{"location":"%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/topdown_ustc/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1a\u4e2d\u56fd\u79d1\u5b66\u6280\u672f\u5927\u5b66 \u6388\u8bfe\u6559\u5e08\uff1a\u90d1\u70c7\u3001\u6768\u575a \u5148\u4fee\u8981\u6c42\uff1a\u64cd\u4f5c\u7cfb\u7edf\uff08\u975e\u5fc5\u9700\uff09 \u7f16\u7a0b\u8bed\u8a00\uff1a\u65e0 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a40 \u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u5e94\u8be5\u662f\u4e2d\u6587\u4e92\u8054\u7f51\u4e0a\u6bd4\u8f83\u706b\u7684\u8ba1\u7b97\u673a\u7f51\u7edc\u8bfe\u4e86\uff0c\u6559\u6750\u91c7\u7528\u795e\u4e66\u8ba1\u7b97\u673a\u7f51\u7edc\uff08\u81ea\u9876\u5411\u4e0b\u65b9\u6cd5\uff09\uff0c\u6388\u8bfe\u98ce\u683c\u66f4\u504f\u5411\u5b9e\u9645\u800c\u975e\u7eaf\u7406\u8bba\uff08 \u5f3a\u70c8\u5efa\u8bae \u5148\u9605\u8bfb\u6559\u6750\u9884\u4e60\u518d\u770b\u8bfe\uff0c\u5426\u5219\u4e0a\u8bfe\u65f6 \u53ef\u80fd 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\u7b2c7\u7248\uff09\uff0c\u673a\u68b0\u5de5\u4e1a\u51fa\u7248\u793e\uff0c2016","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/6031/","text":"MIT 6.031: Software Construction \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u638c\u63e1\u81f3\u5c11\u4e00\u95e8\u7f16\u7a0b\u8bed\u8a00 \u7f16\u7a0b\u8bed\u8a00\uff1aJava \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u7684\u76ee\u6807\u5c31\u662f\u8ba9\u5b66\u751f\u5b66\u4f1a\u5982\u4f55\u5199\u51fa\u9ad8\u8d28\u91cf\u7684\u4ee3\u7801\uff0c\u6240\u8c13\u9ad8\u8d28\u91cf\uff0c\u5219\u662f\u6ee1\u8db3\u4e0b\u9762\u4e09\u4e2a\u76ee\u6807\uff08\u8bfe\u7a0b\u8bbe\u8ba1\u8005\u539f\u8bdd\u590d\u5236\uff0c\u4ee5\u9632\u81ea\u5df1\u7ffb\u8bd1\u66f2\u89e3\u672c\u610f\uff09\uff1a Safe from bugs. Correctness (correct behavior right now) and defensiveness (correct behavior in the future) are required in any software we build. Easy to understand. The code has to communicate to future programmers who need to understand it and make changes in it (fixing bugs or adding new features). That future programmer might be you, months or years from now. You\u2019ll be surprised how much you forget if you don\u2019t write it down, and how much it helps your own future self to have a good design. Ready for change. Software always changes. Some designs make it easy to make changes; others require throwing away and rewriting a lot of code. \u4e3a\u6b64\uff0c\u8fd9\u95e8\u8bfe\u7684\u8bbe\u8ba1\u8005\u4eec\u7cbe\u5fc3\u7f16\u5199\u4e86\u4e00\u672c\u4e66\u6765\u9610\u91ca\u8bf8\u591a\u8f6f\u4ef6\u6784\u5efa\u7684\u6838\u5fc3\u539f\u5219\u4e0e\u524d\u4eba\u603b\u7ed3\u4e0b\u6765\u7684\u5b9d\u8d35\u7ecf\u9a8c\uff0c\u5185\u5bb9\u7ec6\u8282\u5230\u5982\u4f55\u7f16\u5199\u6ce8\u91ca\u548c\u51fd\u6570 Specification\uff0c\u5982\u4f55\u8bbe\u8ba1\u62bd\u8c61\u6570\u636e\u7ed3\u6784\u4ee5\u53ca\u8bf8\u591a\u5e76\u884c\u7f16\u7a0b\u7684\u5185\u5bb9\uff0c\u5e76\u4e14\u4f1a\u8ba9\u4f60\u5728\u7cbe\u5fc3\u8bbe\u8ba1\u7684 Java \u7f16\u7a0b\u9879\u76ee\u91cc\u4f53\u9a8c\u548c\u7ec3\u4e60\u8fd9\u4e9b\u7f16\u7a0b\u6a21\u5f0f\u3002 2016\u5e74\u6625\u5b63\u5b66\u671f\u8fd9\u95e8\u8bfe\u5f00\u6e90\u4e86\u5176\u6240\u6709\u7f16\u7a0b\u4f5c\u4e1a\u7684\u4ee3\u7801\u6846\u67b6\uff0c\u800c\u6700\u65b0\u7684\u8bfe\u7a0b\u6559\u6750\u53ef\u4ee5\u5728\u5176\u6700\u65b0\u7684\u6559\u5b66\u7f51\u7ad9\u4e0a\u627e\u5230\uff0c\u5177\u4f53\u94fe\u63a5\u53c2\u89c1\u4e0b\u65b9\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a 2021spring , 2016spring \u8bfe\u7a0b\u89c6\u9891\uff1a\u65e0 \u8bfe\u7a0b\u6559\u6750\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9\u7684\u8bfe\u7a0b notes \u8bfe\u7a0b\u4f5c\u4e1a\uff1a4 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a + 1 \u4e2a Project \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/MIT6.031-software-construction - GitHub \u4e2d\u3002 @pengzhangzhi \u5b8c\u6210\u4e86\u8fd9\u95e8\u8bfe\u7684\u4f5c\u4e1a\u5e76\u8bb0\u5f55\u4e86\u7b14\u8bb0, \u4ee3\u7801\u5f00\u6e90\u5728 pengzhangzhi/self-taught-CS/Software Construction - Github \u3002","title":"MIT 6.031: Software Construction"},{"location":"%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/6031/#mit-6031-software-construction","text":"","title":"MIT 6.031: Software Construction"},{"location":"%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/6031/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u638c\u63e1\u81f3\u5c11\u4e00\u95e8\u7f16\u7a0b\u8bed\u8a00 \u7f16\u7a0b\u8bed\u8a00\uff1aJava \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u7684\u76ee\u6807\u5c31\u662f\u8ba9\u5b66\u751f\u5b66\u4f1a\u5982\u4f55\u5199\u51fa\u9ad8\u8d28\u91cf\u7684\u4ee3\u7801\uff0c\u6240\u8c13\u9ad8\u8d28\u91cf\uff0c\u5219\u662f\u6ee1\u8db3\u4e0b\u9762\u4e09\u4e2a\u76ee\u6807\uff08\u8bfe\u7a0b\u8bbe\u8ba1\u8005\u539f\u8bdd\u590d\u5236\uff0c\u4ee5\u9632\u81ea\u5df1\u7ffb\u8bd1\u66f2\u89e3\u672c\u610f\uff09\uff1a Safe from bugs. Correctness (correct behavior right now) and defensiveness (correct behavior in the future) are required in any software we build. Easy to understand. The code has to communicate to future programmers who need to understand it and make changes in it (fixing bugs or adding new features). That future programmer might be you, months or years from now. You\u2019ll be surprised how much you forget if you don\u2019t write it down, and how much it helps your own future self to have a good design. Ready for change. Software always changes. Some designs make it easy to make changes; others require throwing away and rewriting a lot of code. \u4e3a\u6b64\uff0c\u8fd9\u95e8\u8bfe\u7684\u8bbe\u8ba1\u8005\u4eec\u7cbe\u5fc3\u7f16\u5199\u4e86\u4e00\u672c\u4e66\u6765\u9610\u91ca\u8bf8\u591a\u8f6f\u4ef6\u6784\u5efa\u7684\u6838\u5fc3\u539f\u5219\u4e0e\u524d\u4eba\u603b\u7ed3\u4e0b\u6765\u7684\u5b9d\u8d35\u7ecf\u9a8c\uff0c\u5185\u5bb9\u7ec6\u8282\u5230\u5982\u4f55\u7f16\u5199\u6ce8\u91ca\u548c\u51fd\u6570 Specification\uff0c\u5982\u4f55\u8bbe\u8ba1\u62bd\u8c61\u6570\u636e\u7ed3\u6784\u4ee5\u53ca\u8bf8\u591a\u5e76\u884c\u7f16\u7a0b\u7684\u5185\u5bb9\uff0c\u5e76\u4e14\u4f1a\u8ba9\u4f60\u5728\u7cbe\u5fc3\u8bbe\u8ba1\u7684 Java \u7f16\u7a0b\u9879\u76ee\u91cc\u4f53\u9a8c\u548c\u7ec3\u4e60\u8fd9\u4e9b\u7f16\u7a0b\u6a21\u5f0f\u3002 2016\u5e74\u6625\u5b63\u5b66\u671f\u8fd9\u95e8\u8bfe\u5f00\u6e90\u4e86\u5176\u6240\u6709\u7f16\u7a0b\u4f5c\u4e1a\u7684\u4ee3\u7801\u6846\u67b6\uff0c\u800c\u6700\u65b0\u7684\u8bfe\u7a0b\u6559\u6750\u53ef\u4ee5\u5728\u5176\u6700\u65b0\u7684\u6559\u5b66\u7f51\u7ad9\u4e0a\u627e\u5230\uff0c\u5177\u4f53\u94fe\u63a5\u53c2\u89c1\u4e0b\u65b9\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/6031/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a 2021spring , 2016spring \u8bfe\u7a0b\u89c6\u9891\uff1a\u65e0 \u8bfe\u7a0b\u6559\u6750\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9\u7684\u8bfe\u7a0b notes \u8bfe\u7a0b\u4f5c\u4e1a\uff1a4 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a + 1 \u4e2a Project","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/6031/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/MIT6.031-software-construction - 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GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/","text":"Foreword The English version is still under development, please check this issue if you want to contribute. This is a self-learning guide to computer science, and a memento of my three years of self-learning at university. It is also a gift to the young students at Peking University. It would be a great encouragement and comfort to me if this book could be of even the slightest help to you in your college life. The book is currently organized to include the following sections (if you have other good suggestions, or would like to join the ranks of contributors, please feel free to email zhongyinmin@pku.edu.cn or ask questions in the issue). Productivity Toolkit: IDE, VPN, StackOverflow, Git, Github, Vim, Latex, GNU Make and so on. Environment configuration: PC/Server development environment setup, DevOps tutorials and so on. Book recommendations: Those who have read the CSAPP must have realized the importance of good books. I will list links to books and resources in different areas of Computer Science that I find rewarding to read. List of high quality CS courses : I will summarize all the high quality foreign CS courses I have taken into different categories and give relevant self-learning advice. Most of them will have a separate repository containing relevant resources as well as my homework/project implementations. The place where dreams start \u2014\u2014 CS61A In my freshman year, I was a novice who knew nothing about computers. I installed a giant IDE Visual Studio and fight with OJ every day. With my high school maths background, I did pretty well in maths courses, but I felt struggled to learn courses in my major. When it came to programming, all I could do was open up that clunky IDE, create a new project that I didn't know exactly what it was for, and then cin , cout , for loops, and then CE, RE, WA loops. I was in a state where I was desperately trying to learn well but I didn't know how to learn. I listened carefully in class but I couldn't solve the homework problems. I spent almost all my spare time doing the homework after class, but the results were disappointing. I still retain the source code of the project for Introduction to Computing course \u2014\u2014 a single 1200-line C++ file with no header files, no class abstraction, no unit tests, no makefile, no version control. The only good thing is that it can run, the disadvantage is the complement of \"can run\". For a while I wondered if I wasn't cut out for computer science, as all my childhood imaginings of geekiness had been completely ruined by my first semester's experience. It all turned around during the winter break of my freshman year, when I had a hankering to learn Python. I overheard someone recommend CS61A, a freshman introductory course at UC Berkeley on Python. I'll never forget that day, when I opened the CS61A course website. It was like Columbus discovering a new continent, and I opened the door to a new world. I finished the course in 3 weeks and for the first time I felt that CS could be so fulfilling and interesting, and I was shocked that there existed such a great course in the world. To avoid any suspicion of pandering to foreign courses, I will tell you about my experience of studying CS61A from the perspective of a pure student. Course website developed by course staffs : The course website integrates all the course resources into one, with a well organised course schedule, links to all slides, recorded videos and homework, detailed and clear syllabus, list of exams and solutions from previous years. Aesthetics aside, this website is so convenient for students. Textbook written by course instructor : The course instructor has adapted the classic MIT textbook Structure and Interpretation of Computer Programs (SICP) into Python (the original textbook was based on Scheme). This is a great way to ensure that the classroom content is consistent with the textbook, while adding more details. The entire book is open source and can be read directly online. Various, comprehensive and interesting homework : There are 14 labs to reinforce the knowledge gained in class, 10 homework assignments to practice, and 4 projects each with thousands of lines of code, all with well-organized skeleton code and babysitting instructions. Unlike the old-school OJ and Word document assignments, each lab/homework/project has a detailed handout document, fully automated grading scripts, and CS61A staffs have even developed an automated assignment submission and grading system . Of course, one might say \"How much can you learn from a project where most of code are written by your teaching assistants?\" . For someone who is new to CS and even stumbling over installing Python, this well-developed skeleton code allows students to focus on reinforcing the core knowledge they've learned in class, but also gives them a sense of achievement that they already can make a little game despite of learning Python only for a month. It also gives them the opportunity to read and learn from other people's high quality code so that they can reuse it later. I think in the freshman year, this kind of skeleton code is absolutely beneficial. The only bad thing perhaps is for the instructors and teaching assistants, as developing such assignments can conceivably require a considerable time commitment. Weekly discussion sessions : The teaching assistants will explain the difficult knowledge in class and add some supplementary materials which may not be covered in class. Also, there will be exercises from exams of previous years. All the exercises are written in LaTeX with solutions. In CS61A, You don't need any prerequesites about CS at all. You just need to pay attention, spend time and work hard. The feeling that you do not know what to do, that you are not getting anything in return for all the time you put in, is gone. It suited me so well that I fell in love with self-learning. Imagine that if someone could chew up the hard knowledge and present it to you in a vivid and straightforward way, with so many fancy and varied projects to reinforce your theoretical knowledge, you'd think they were really trying their best to make you fully grasp the course, and it was even an insult to the course builders not to learn it well. If you think I'm exaggerating, start with CS61A , because it's where my dreams began. Why write this book? In the 2020 Fall semester, I worked as a teaching assistant for the class Introduction to Computer Systems at Peking University. At that time, I had been studying totally on my own for over a year. I enjoyed this style of learning immensely. To share this joy, I have made a CS Self-learning Materials List for students in my seminar. It was purely on a whim at the time, as I wouldn't dare to encourage my students to skip classes and study on their own. But after another year of maintenance, the list has become quite comprehensive, covering most of the courses in Computer Science, Artificial Intelligence and Soft Engineering, and I have built separate repositories for each course, summarising the self-learning materials that I used. In my last college year, when I opened up my curriculum book, I realized that it was already a subset of my self-learning list. By then, it was only two and a half years after I had started my self-learning journey. Then, a bold idea came to my mind: perhaps I could create a self-learning book, write down the difficulty I encountered and the interest I found during these years of self-learning, hoping to make it easy for students who may also enjoy self-learning to start their wonderful self-learning journey. If you can build up the whole CS foundation in less than three years, have relatively solid mathematical skills and coding ability, experience dozens of projects with thousands of lines of code, master at least C/C++/Java/JS/Python/Go/Rust and other mainstream programming languages, have a good understanding of algorithms, circuits, architectures, networks, operating systems, compilers, artificial intelligence, machine learning, computer vision, natural language processing, reinforcement learning, cryptography, information theory, game theory, numerical analysis, statistics, distributed systems, parallel computing, database systems, computer graphics, web development, cloud computing, supercomputing etc. I think you will be confident enough to choose the area you are interested in, and you will be quite competitive in both industry and academia. I firmly believe that if you have read to this line, you do not lack the ability and committment to learn CS well, you just need a good teacher to teach you a good course. And I will try my best to pick such courses for you, based on my three years of experience. Pros For me, the biggest advantage of self-learning is that I can adjust the pace of learning entirely according to my own progress. For difficult parts, I can watch the videos over and over again, Google it online and ask questions on StackOverflow until I have it all figured out. For those that I mastered relatively quickly, I could skip them at twice or even three times the speed. Another great thing about self-learning is that you can learn from different perspectives. I have taken core courses such as architectures, networking, operating systems, and compilers from different universities. Different instructors may have different views on the same knowledge, which will broaden your horizon. A third advantage of self-learning is that you do not need to go to the class, listening to the boring lectures. Cons Of course, as a big fan of self-learning, I have to admit that it has its disadvantages. The first is the difficulty of communication. I'm actually a very keen questioner, and I like to follow up all the points I don't understand. But when you're facing a screen and you hear a teacher talking about something you don't understand, you can't go to the other end of the network and ask him or her for clarification. I try to mitigate this by thinking independently and making good use of Google, but it would be great to have a few friends to study together. You can refer to README for more information on participating a community group. The second thing is that these courses are basically in English. From the videos to the slides to the assignments, all in English. You may struggle at first, but I think it's a challenge that if you overcome, it will be extremely rewarding. Because at the moment, as reluctant as I am, I have to admit that in computer science, a lot of high quality documentation, forums and websites are all in English. The third, and I think the most difficult one, is self-discipline. Because have no DDL can sometimes be a really scary thing, especially when you get deeper, many foreign courses are quite difficult. You have to be self-driven enough to force yourself to settle down, read dozens of pages of Project Handout, understand thousands of lines of skeleton code and endure hours of debugging time. With no credits, no grades, no teachers, no classmates, just one belief - that you are getting better. Who is this book for? As I said in the beginning, anyone who is interested in learning computer science on their own can refer to this book. If you already have some basic skills and are just interested in a particular area, you can selectively pick and choose what you are interested in to study. Of course, if you are a novice who knows nothing about computers like I did back then, and just begin your college journey, I hope this book will be your cheat sheet to get the knowledge and skills you need in the least amount of time. In a way, this book is more like a course search engine ordered according to my experience, helping you to learn high quality CS courses from the world's top universities without leaving home. Of course, as an undergraduate student who has not yet graduated, I feel that I am not in a position nor have the right to preach one way of learning. I just hope that this material will help those who are also self-motivated and persistent to gain a richer, more varied and satisfying college life. Special thanks I would like to express my sincere gratitude to all the professors who have made their courses public for free. These courses are the culmination of decades of their teaching careers, and they have chosen to selflessly make such a high quality CS education available to all. Without them, my university life would not have been as fulfilling and enjoyable. Many of the professors would even reply with hundreds of words in length after I had sent them a thank you email, which really touched me beyond words. They also inspired me all the time that if decide to do something, do it with all heart and soul. Want to join as a contributor? There is a limit to how much one person can do, and this book was written by me under a heavy research schedule, so there are inevitably imperfections. In addition, as I work in the area of systems, many of the courses focus on systems, and there is relatively little content related to advanced mathematics, computing theory, and advanced algorithms. If any of you would like to share your self-learning experience and resources in other areas, you can directly initiate a Pull Request in the project, or feel free to contact me by email ( zhongyinmin@pku.edu.cn ).","title":"Foreword"},{"location":"en/#foreword","text":"The English version is still under development, please check this issue if you want to contribute. This is a self-learning guide to computer science, and a memento of my three years of self-learning at university. It is also a gift to the young students at Peking University. It would be a great encouragement and comfort to me if this book could be of even the slightest help to you in your college life. The book is currently organized to include the following sections (if you have other good suggestions, or would like to join the ranks of contributors, please feel free to email zhongyinmin@pku.edu.cn or ask questions in the issue). Productivity Toolkit: IDE, VPN, StackOverflow, Git, Github, Vim, Latex, GNU Make and so on. Environment configuration: PC/Server development environment setup, DevOps tutorials and so on. Book recommendations: Those who have read the CSAPP must have realized the importance of good books. I will list links to books and resources in different areas of Computer Science that I find rewarding to read. List of high quality CS courses : I will summarize all the high quality foreign CS courses I have taken into different categories and give relevant self-learning advice. Most of them will have a separate repository containing relevant resources as well as my homework/project implementations.","title":"Foreword"},{"location":"en/#the-place-where-dreams-start-cs61a","text":"In my freshman year, I was a novice who knew nothing about computers. I installed a giant IDE Visual Studio and fight with OJ every day. With my high school maths background, I did pretty well in maths courses, but I felt struggled to learn courses in my major. When it came to programming, all I could do was open up that clunky IDE, create a new project that I didn't know exactly what it was for, and then cin , cout , for loops, and then CE, RE, WA loops. I was in a state where I was desperately trying to learn well but I didn't know how to learn. I listened carefully in class but I couldn't solve the homework problems. I spent almost all my spare time doing the homework after class, but the results were disappointing. I still retain the source code of the project for Introduction to Computing course \u2014\u2014 a single 1200-line C++ file with no header files, no class abstraction, no unit tests, no makefile, no version control. The only good thing is that it can run, the disadvantage is the complement of \"can run\". For a while I wondered if I wasn't cut out for computer science, as all my childhood imaginings of geekiness had been completely ruined by my first semester's experience. It all turned around during the winter break of my freshman year, when I had a hankering to learn Python. I overheard someone recommend CS61A, a freshman introductory course at UC Berkeley on Python. I'll never forget that day, when I opened the CS61A course website. It was like Columbus discovering a new continent, and I opened the door to a new world. I finished the course in 3 weeks and for the first time I felt that CS could be so fulfilling and interesting, and I was shocked that there existed such a great course in the world. To avoid any suspicion of pandering to foreign courses, I will tell you about my experience of studying CS61A from the perspective of a pure student. Course website developed by course staffs : The course website integrates all the course resources into one, with a well organised course schedule, links to all slides, recorded videos and homework, detailed and clear syllabus, list of exams and solutions from previous years. Aesthetics aside, this website is so convenient for students. Textbook written by course instructor : The course instructor has adapted the classic MIT textbook Structure and Interpretation of Computer Programs (SICP) into Python (the original textbook was based on Scheme). This is a great way to ensure that the classroom content is consistent with the textbook, while adding more details. The entire book is open source and can be read directly online. Various, comprehensive and interesting homework : There are 14 labs to reinforce the knowledge gained in class, 10 homework assignments to practice, and 4 projects each with thousands of lines of code, all with well-organized skeleton code and babysitting instructions. Unlike the old-school OJ and Word document assignments, each lab/homework/project has a detailed handout document, fully automated grading scripts, and CS61A staffs have even developed an automated assignment submission and grading system . Of course, one might say \"How much can you learn from a project where most of code are written by your teaching assistants?\" . For someone who is new to CS and even stumbling over installing Python, this well-developed skeleton code allows students to focus on reinforcing the core knowledge they've learned in class, but also gives them a sense of achievement that they already can make a little game despite of learning Python only for a month. It also gives them the opportunity to read and learn from other people's high quality code so that they can reuse it later. I think in the freshman year, this kind of skeleton code is absolutely beneficial. The only bad thing perhaps is for the instructors and teaching assistants, as developing such assignments can conceivably require a considerable time commitment. Weekly discussion sessions : The teaching assistants will explain the difficult knowledge in class and add some supplementary materials which may not be covered in class. Also, there will be exercises from exams of previous years. All the exercises are written in LaTeX with solutions. In CS61A, You don't need any prerequesites about CS at all. You just need to pay attention, spend time and work hard. The feeling that you do not know what to do, that you are not getting anything in return for all the time you put in, is gone. It suited me so well that I fell in love with self-learning. Imagine that if someone could chew up the hard knowledge and present it to you in a vivid and straightforward way, with so many fancy and varied projects to reinforce your theoretical knowledge, you'd think they were really trying their best to make you fully grasp the course, and it was even an insult to the course builders not to learn it well. If you think I'm exaggerating, start with CS61A , because it's where my dreams began.","title":"The place where dreams start \u2014\u2014 CS61A"},{"location":"en/#why-write-this-book","text":"In the 2020 Fall semester, I worked as a teaching assistant for the class Introduction to Computer Systems at Peking University. At that time, I had been studying totally on my own for over a year. I enjoyed this style of learning immensely. To share this joy, I have made a CS Self-learning Materials List for students in my seminar. It was purely on a whim at the time, as I wouldn't dare to encourage my students to skip classes and study on their own. But after another year of maintenance, the list has become quite comprehensive, covering most of the courses in Computer Science, Artificial Intelligence and Soft Engineering, and I have built separate repositories for each course, summarising the self-learning materials that I used. In my last college year, when I opened up my curriculum book, I realized that it was already a subset of my self-learning list. By then, it was only two and a half years after I had started my self-learning journey. Then, a bold idea came to my mind: perhaps I could create a self-learning book, write down the difficulty I encountered and the interest I found during these years of self-learning, hoping to make it easy for students who may also enjoy self-learning to start their wonderful self-learning journey. If you can build up the whole CS foundation in less than three years, have relatively solid mathematical skills and coding ability, experience dozens of projects with thousands of lines of code, master at least C/C++/Java/JS/Python/Go/Rust and other mainstream programming languages, have a good understanding of algorithms, circuits, architectures, networks, operating systems, compilers, artificial intelligence, machine learning, computer vision, natural language processing, reinforcement learning, cryptography, information theory, game theory, numerical analysis, statistics, distributed systems, parallel computing, database systems, computer graphics, web development, cloud computing, supercomputing etc. I think you will be confident enough to choose the area you are interested in, and you will be quite competitive in both industry and academia. I firmly believe that if you have read to this line, you do not lack the ability and committment to learn CS well, you just need a good teacher to teach you a good course. And I will try my best to pick such courses for you, based on my three years of experience.","title":"Why write this book?"},{"location":"en/#pros","text":"For me, the biggest advantage of self-learning is that I can adjust the pace of learning entirely according to my own progress. For difficult parts, I can watch the videos over and over again, Google it online and ask questions on StackOverflow until I have it all figured out. For those that I mastered relatively quickly, I could skip them at twice or even three times the speed. Another great thing about self-learning is that you can learn from different perspectives. I have taken core courses such as architectures, networking, operating systems, and compilers from different universities. Different instructors may have different views on the same knowledge, which will broaden your horizon. A third advantage of self-learning is that you do not need to go to the class, listening to the boring lectures.","title":"Pros"},{"location":"en/#cons","text":"Of course, as a big fan of self-learning, I have to admit that it has its disadvantages. The first is the difficulty of communication. I'm actually a very keen questioner, and I like to follow up all the points I don't understand. But when you're facing a screen and you hear a teacher talking about something you don't understand, you can't go to the other end of the network and ask him or her for clarification. I try to mitigate this by thinking independently and making good use of Google, but it would be great to have a few friends to study together. You can refer to README for more information on participating a community group. The second thing is that these courses are basically in English. From the videos to the slides to the assignments, all in English. You may struggle at first, but I think it's a challenge that if you overcome, it will be extremely rewarding. Because at the moment, as reluctant as I am, I have to admit that in computer science, a lot of high quality documentation, forums and websites are all in English. The third, and I think the most difficult one, is self-discipline. Because have no DDL can sometimes be a really scary thing, especially when you get deeper, many foreign courses are quite difficult. You have to be self-driven enough to force yourself to settle down, read dozens of pages of Project Handout, understand thousands of lines of skeleton code and endure hours of debugging time. With no credits, no grades, no teachers, no classmates, just one belief - that you are getting better.","title":"Cons"},{"location":"en/#who-is-this-book-for","text":"As I said in the beginning, anyone who is interested in learning computer science on their own can refer to this book. If you already have some basic skills and are just interested in a particular area, you can selectively pick and choose what you are interested in to study. Of course, if you are a novice who knows nothing about computers like I did back then, and just begin your college journey, I hope this book will be your cheat sheet to get the knowledge and skills you need in the least amount of time. In a way, this book is more like a course search engine ordered according to my experience, helping you to learn high quality CS courses from the world's top universities without leaving home. Of course, as an undergraduate student who has not yet graduated, I feel that I am not in a position nor have the right to preach one way of learning. I just hope that this material will help those who are also self-motivated and persistent to gain a richer, more varied and satisfying college life.","title":"Who is this book for?"},{"location":"en/#special-thanks","text":"I would like to express my sincere gratitude to all the professors who have made their courses public for free. These courses are the culmination of decades of their teaching careers, and they have chosen to selflessly make such a high quality CS education available to all. Without them, my university life would not have been as fulfilling and enjoyable. Many of the professors would even reply with hundreds of words in length after I had sent them a thank you email, which really touched me beyond words. They also inspired me all the time that if decide to do something, do it with all heart and soul.","title":"Special thanks"},{"location":"en/#want-to-join-as-a-contributor","text":"There is a limit to how much one person can do, and this book was written by me under a heavy research schedule, so there are inevitably imperfections. In addition, as I work in the area of systems, many of the courses focus on systems, and there is relatively little content related to advanced mathematics, computing theory, and advanced algorithms. If any of you would like to share your self-learning experience and resources in other areas, you can directly initiate a Pull Request in the project, or feel free to contact me by email ( zhongyinmin@pku.edu.cn ).","title":"Want to join as a contributor?"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/","text":"\u4e00\u4e2a\u4ec5\u4f9b\u53c2\u8003\u7684 CS \u5b66\u4e60\u89c4\u5212 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Calculus Course \u548c 18.06: Linear Algebra \u7684\u8bfe\u7a0b notes\uff0c\u81f3\u5c11\u4e8e\u6211\u800c\u8a00\uff0c\u5b83\u5e2e\u52a9\u6211\u6df1\u523b\u7406\u89e3\u4e86\u5fae\u79ef\u5206\u548c\u7ebf\u6027\u4ee3\u6570\u7684\u8bb8\u591a\u672c\u8d28\u3002\u987a\u9053\u518d\u5b89\u5229\u4e00\u4e2a\u6cb9\u7ba1\u6570\u5b66\u7f51\u7ea2 3Blue1Brown \uff0c\u4ed6\u7684\u9891\u9053\u6709\u5f88\u591a\u7528\u751f\u52a8\u5f62\u8c61\u7684\u52a8\u753b\u9610\u91ca\u6570\u5b66\u672c\u8d28\u5185\u6838\u7684\u89c6\u9891\uff0c\u517c\u5177\u6df1\u5ea6\u548c\u5e7f\u5ea6\uff0c\u8d28\u91cf\u975e\u5e38\u9ad8\u3002 \u4fe1\u606f\u8bba\u5165\u95e8 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u53ca\u65e9\u4e86\u89e3\u4e00\u4e9b\u4fe1\u606f\u8bba\u7684\u57fa\u7840\u77e5\u8bc6\uff0c\u6211\u89c9\u5f97\u662f\u5927\u6709\u88e8\u76ca\u7684\u3002\u4f46\u5927\u591a\u4fe1\u606f\u8bba\u8bfe\u7a0b\u90fd\u9762\u5411\u9ad8\u5e74\u7ea7\u672c\u79d1\u751f\u751a\u81f3\u7814\u7a76\u751f\uff0c\u5bf9\u65b0\u624b\u6781\u4e0d\u53cb\u597d\u3002\u800c MIT \u7684 6.050J: Information theory and Entropy \u8fd9\u95e8\u8bfe\u6b63\u662f\u4e3a\u5927\u4e00\u65b0\u751f\u91cf\u8eab\u5b9a\u5236\u7684\uff0c\u51e0\u4e4e\u6ca1\u6709\u5148\u4fee\u8981\u6c42\uff0c\u6db5\u76d6\u4e86\u7f16\u7801\u3001\u538b\u7f29\u3001\u901a\u4fe1\u3001\u4fe1\u606f\u71b5\u7b49\u7b49\u5185\u5bb9\uff0c\u975e\u5e38\u6709\u8da3\u3002 \u6570\u5b66\u8fdb\u9636 \u79bb\u6563\u6570\u5b66\u4e0e\u6982\u7387\u8bba \u96c6\u5408\u8bba\u3001\u56fe\u8bba\u3001\u6982\u7387\u8bba\u7b49\u7b49\u662f\u7b97\u6cd5\u63a8\u5bfc\u4e0e\u8bc1\u660e\u7684\u91cd\u8981\u5de5\u5177\uff0c\u4e5f\u662f\u540e\u7eed\u9ad8\u9636\u6570\u5b66\u8bfe\u7a0b\u7684\u57fa\u7840\u3002\u4f46\u6211\u89c9\u5f97\u8fd9\u7c7b\u8bfe\u7a0b\u7684\u8bb2\u6388\u5f88\u5bb9\u6613\u843d\u5165\u7406\u8bba\u5316\u4e0e\u5f62\u5f0f\u5316\u7684\u7aa0\u81fc\uff0c\u8ba9\u8bfe\u5802\u6210\u4e3a\u5b9a\u7406\u7ed3\u8bba\u7684\u5806\u780c\uff0c\u800c\u65e0\u6cd5\u4f7f\u5b66\u751f\u6df1\u523b\u628a\u63e1\u7406\u8bba\u7684\u672c\u8d28\uff0c\u8fdb\u800c\u9020\u6210\u5b66\u4e86\u5c31\u80cc\uff0c\u8003\u4e86\u5c31\u5fd8\u7684\u602a\u5708\u3002\u5982\u679c\u80fd\u5728\u7406\u8bba\u6559\u5b66\u4e2d\u7a7f\u63d2\u7b97\u6cd5\u8fd0\u7528\u5b9e\u4f8b\uff0c\u5b66\u751f\u5728\u62d3\u5c55\u7b97\u6cd5\u77e5\u8bc6\u7684\u540c\u65f6\u4e5f\u80fd\u7aa5\u89c1\u7406\u8bba\u7684\u529b\u91cf\u548c\u9b45\u529b\u3002 UCB CS70 : discrete Math and probability theory \u548c UCB CS126 : Probability theory \u662f UC Berkeley \u7684\u6982\u7387\u8bba\u8bfe\u7a0b\uff0c\u524d\u8005\u8986\u76d6\u4e86\u79bb\u6563\u6570\u5b66\u548c\u6982\u7387\u8bba\u57fa\u7840\uff0c\u540e\u8005\u5219\u6d89\u53ca\u968f\u673a\u8fc7\u7a0b\u4ee5\u53ca\u6df1\u5165\u7684\u7406\u8bba\u5185\u5bb9\u3002\u4e24\u8005\u90fd\u975e\u5e38\u6ce8\u91cd\u7406\u8bba\u548c\u5b9e\u8df5\u7684\u7ed3\u5408\uff0c\u6709\u4e30\u5bcc\u7684\u7b97\u6cd5\u5b9e\u9645\u8fd0\u7528\u5b9e\u4f8b\uff0c\u540e\u8005\u8fd8\u6709\u5927\u91cf\u7684 Python \u7f16\u7a0b\u4f5c\u4e1a\u6765\u8ba9\u5b66\u751f\u8fd0\u7528\u6982\u7387\u8bba\u7684\u77e5\u8bc6\u89e3\u51b3\u5b9e\u9645\u95ee\u9898\u3002 \u6570\u503c\u5206\u6790 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u57f9\u517b\u8ba1\u7b97\u601d\u7ef4\u662f\u5f88\u91cd\u8981\u7684\uff0c\u5b9e\u9645\u95ee\u9898\u7684\u5efa\u6a21\u3001\u79bb\u6563\u5316\uff0c\u8ba1\u7b97\u673a\u7684\u6a21\u62df\u3001\u5206\u6790\uff0c\u662f\u4e00\u9879\u5f88\u91cd\u8981\u7684\u80fd\u529b\u3002\u800c\u8fd9\u4e24\u5e74\u5f00\u59cb\u98ce\u9761\u7684\uff0c\u7531 MIT \u6253\u9020\u7684 Julia \u7f16\u7a0b\u8bed\u8a00\u4ee5\u5176 C \u4e00\u6837\u7684\u901f\u5ea6\u548c Python \u4e00\u6837\u53cb\u597d\u7684\u8bed\u6cd5\u5728\u6570\u503c\u8ba1\u7b97\u9886\u57df\u6709\u4e00\u7edf\u5929\u4e0b\u4e4b\u52bf\uff0cMIT \u7684\u8bb8\u591a\u6570\u5b66\u8bfe\u7a0b\u4e5f\u5f00\u59cb\u7528 Julia \u4f5c\u4e3a\u6559\u5b66\u5de5\u5177\uff0c\u628a\u8270\u6df1\u7684\u6570\u5b66\u7406\u8bba\u7528\u76f4\u89c2\u6e05\u6670\u7684\u4ee3\u7801\u5c55\u793a\u51fa\u6765\u3002 ComputationalThinking \u662f MIT \u5f00\u8bbe\u7684\u4e00\u95e8\u8ba1\u7b97\u601d\u7ef4\u5165\u95e8\u8bfe\uff0c\u6240\u6709\u8bfe\u7a0b\u5185\u5bb9\u5168\u90e8\u5f00\u6e90\uff0c\u53ef\u4ee5\u5728\u8bfe\u7a0b\u7f51\u7ad9\u76f4\u63a5\u8bbf\u95ee\u3002\u8fd9\u95e8\u8bfe\u5229\u7528 Julia \u7f16\u7a0b\u8bed\u8a00\uff0c\u5728\u56fe\u50cf\u5904\u7406\u3001\u793e\u4f1a\u79d1\u5b66\u4e0e\u6570\u636e\u79d1\u5b66\u3001\u6c14\u5019\u5b66\u5efa\u6a21\u4e09\u4e2a topic \u4e0b\u5e26\u9886\u5b66\u751f\u7406\u89e3\u7b97\u6cd5\u3001\u6570\u5b66\u5efa\u6a21\u3001\u6570\u636e\u5206\u6790\u3001\u4ea4\u4e92\u8bbe\u8ba1\u3001\u56fe\u4f8b\u5c55\u793a\uff0c\u8ba9\u5b66\u751f\u4f53\u9a8c\u8ba1\u7b97\u4e0e\u79d1\u5b66\u7684\u7f8e\u5999\u7ed3\u5408\u3002\u5185\u5bb9\u867d\u7136\u4e0d\u96be\uff0c\u4f46\u7ed9\u6211\u6700\u6df1\u523b\u7684\u611f\u53d7\u5c31\u662f\uff0c\u79d1\u5b66\u7684\u9b45\u529b\u5e76\u4e0d\u662f\u6545\u5f04\u7384\u865a\u7684\u8270\u6df1\u7406\u8bba\uff0c\u4e0d\u662f\u8bd8\u5c48\u8071\u7259\u7684\u672f\u8bed\u884c\u8bdd\uff0c\u800c\u662f\u7528\u76f4\u89c2\u751f\u52a8\u7684\u6848\u4f8b\uff0c\u7528\u7b80\u7ec3\u6df1\u523b\u7684\u8bed\u8a00\uff0c\u8ba9\u6bcf\u4e2a\u666e\u901a\u4eba\u90fd\u80fd\u7406\u89e3\u3002 \u4e0a\u5b8c\u4e0a\u9762\u7684\u4f53\u9a8c\u8bfe\u4e4b\u540e\uff0c\u5982\u679c\u610f\u72b9\u672a\u5c3d\u7684\u8bdd\uff0c\u4e0d\u59a8\u8bd5\u8bd5 MIT \u7684 18.330 : Introduction to numerical analysis \uff0c\u8fd9\u95e8\u8bfe\u7684\u7f16\u7a0b\u4f5c\u4e1a\u540c\u6837\u4f1a\u7528 Julia \u7f16\u7a0b\u8bed\u8a00\uff0c\u4e0d\u8fc7\u96be\u5ea6\u548c\u6df1\u5ea6\u4e0a\u90fd\u4e0a\u4e86\u4e00\u4e2a\u53f0\u9636\u3002\u5185\u5bb9\u6d89\u53ca\u4e86\u6d6e\u70b9\u7f16\u7801\u3001Root finding\u3001\u7ebf\u6027\u7cfb\u7edf\u3001\u5fae\u5206\u65b9\u7a0b\u7b49\u7b49\u65b9\u9762\uff0c\u6574\u95e8\u8bfe\u7684\u4e3b\u65e8\u5c31\u662f\u8ba9\u4f60\u5229\u7528\u79bb\u6563\u5316\u7684\u8ba1\u7b97\u673a\u8868\u793a\u53bb\u4f30\u8ba1\u548c\u903c\u8fd1\u4e00\u4e2a\u6570\u5b66\u4e0a\u8fde\u7eed\u7684\u6982\u5ff5\u3002\u8fd9\u95e8\u8bfe\u7684\u6559\u6388\u8fd8\u4e13\u95e8\u64b0\u5199\u4e86\u4e00\u672c\u914d\u5957\u7684\u5f00\u6e90\u6559\u6750 Fundamentals of Numerical Computation \uff0c\u91cc\u9762\u9644\u6709\u4e30\u5bcc\u7684 Julia \u4ee3\u7801\u5b9e\u4f8b\u548c\u4e25\u8c28\u7684\u516c\u5f0f\u63a8\u5bfc\u3002 \u5982\u679c\u4f60\u8fd8\u610f\u72b9\u672a\u5c3d\u7684\u8bdd\uff0c\u8fd8\u6709 MIT \u7684\u6570\u503c\u5206\u6790\u7814\u7a76\u751f\u8bfe\u7a0b 18.335: Introduction to numerical method \u4f9b\u4f60\u53c2\u8003\u3002 \u5fae\u5206\u65b9\u7a0b \u5982\u679c\u4e16\u95f4\u4e07\u7269\u7684\u8fd0\u52a8\u53d1\u5c55\u90fd\u80fd\u7528\u65b9\u7a0b\u6765\u523b\u753b\u548c\u63cf\u8ff0\uff0c\u8fd9\u662f\u4e00\u4ef6\u591a\u4e48\u9177\u7684\u4e8b\u60c5\u5440\uff01\u867d\u7136\u51e0\u4e4e\u4efb\u4f55\u4e00\u6240\u5b66\u6821\u7684 CS \u57f9\u517b\u65b9\u6848\u4e2d\u90fd\u6ca1\u6709\u5fae\u5206\u65b9\u7a0b\u76f8\u5173\u7684\u5fc5\u4fee\u8bfe\u7a0b\uff0c\u4f46\u6211\u8fd8\u662f\u89c9\u5f97\u638c\u63e1\u5b83\u4f1a\u8d4b\u4e88\u4f60\u4e00\u4e2a\u65b0\u7684\u89c6\u89d2\u6765\u5ba1\u89c6\u8fd9\u4e2a\u4e16\u754c\u3002 \u7531\u4e8e\u5fae\u5206\u65b9\u7a0b\u4e2d\u5f80\u5f80\u4f1a\u7528\u5230\u5f88\u591a\u590d\u53d8\u51fd\u6570\u7684\u77e5\u8bc6\uff0c\u6240\u4ee5\u5927\u5bb6\u53ef\u4ee5\u53c2\u8003 MIT18.04: Complex variables functions \u7684\u8bfe\u7a0b notes \u6765\u8865\u9f50\u5148\u4fee\u77e5\u8bc6\u3002 MIT18.03: differential equations ) \u4e3b\u8981\u8986\u76d6\u4e86\u5e38\u5fae\u5206\u65b9\u7a0b\u7684\u6c42\u89e3\uff0c\u5728\u6b64\u57fa\u7840\u4e4b\u4e0a MIT18.152: Partial differential equations ) \u5219\u4f1a\u6df1\u5165\u504f\u5fae\u5206\u65b9\u7a0b\u7684\u5efa\u6a21\u4e0e\u6c42\u89e3\u3002\u638c\u63e1\u4e86\u5fae\u5206\u65b9\u7a0b\u8fd9\u4e00\u6709\u5229\u5de5\u5177\uff0c\u76f8\u4fe1\u5bf9\u4e8e\u4f60\u7684\u5b9e\u9645\u95ee\u9898\u7684\u5efa\u6a21\u80fd\u529b\u4ee5\u53ca\u4ece\u4f17\u591a\u566a\u58f0\u53d8\u91cf\u4e2d\u628a\u63e1\u672c\u8d28\u7684\u76f4\u89c9\u90fd\u4f1a\u6709\u5f88\u5927\u5e2e\u52a9\u3002 \u6570\u5b66\u9ad8\u9636 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u6211\u7ecf\u5e38\u542c\u5230\u6570\u5b66\u65e0\u7528\u8bba\u7684\u8bba\u65ad\uff0c\u5bf9\u6b64\u6211\u4e0d\u6562\u82df\u540c\u4f46\u4e5f\u65e0\u6743\u53cd\u5bf9\uff0c\u4f46\u82e5\u51e1\u4e8b\u90fd\u786c\u8981\u4e89\u51fa\u4e2a\u6709\u7528\u548c\u65e0\u7528\u7684\u533a\u522b\u6765\uff0c\u5012\u4e5f\u7740\u5b9e\u65e0\u8da3\uff0c\u56e0\u6b64\u4e0b\u9762\u8fd9\u4e9b\u9762\u5411\u9ad8\u5e74\u7ea7\u751a\u81f3\u7814\u7a76\u751f\u7684\u6570\u5b66\u8bfe\u7a0b\uff0c\u5927\u5bb6\u6309\u5174\u8da3\u81ea\u53d6\u6240\u9700\u3002 \u51f8\u4f18\u5316 Standford EE364A: Convex Optimization \u4fe1\u606f\u8bba MIT6.441: Information Theory \u5e94\u7528\u7edf\u8ba1\u5b66 MIT18.650: Statistics for Applications \u521d\u7b49\u6570\u8bba MIT18.781: Theory of Numbers \u5bc6\u7801\u5b66 Standford CS255: Cryptography \u7f16\u7a0b\u5165\u95e8 Languages are tools, you choose the right tool to do the right thing. Since there's no universally perfect tool, there's no universally perfect language. Shell MIT-Missing-Semester Python Harvard CS50: This is CS50x UCB CS61A: Structure and Interpretation of Computer Programs C++ Stanford CS106B/X: Programming Abstractions Stanford CS106L: Standard C++ Programming Rust Stanford CS110L: Safety in Systems Programming OCaml Cornell CS3110 textbook: Functional Programming in OCaml \u7535\u5b50\u57fa\u7840 \u7535\u8def\u57fa\u7840 \u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u4e86\u89e3\u4e00\u4e9b\u57fa\u7840\u7684\u7535\u8def\u77e5\u8bc6\uff0c\u611f\u53d7\u4ece\u4f20\u611f\u5668\u6536\u96c6\u6570\u636e\u5230\u6570\u636e\u5206\u6790\u518d\u5230\u7b97\u6cd5\u9884\u6d4b\u6574\u6761\u6d41\u6c34\u7ebf\uff0c\u5bf9\u4e8e\u540e\u7eed\u77e5\u8bc6\u7684\u5b66\u4e60\u4ee5\u53ca\u8ba1\u7b97\u601d\u7ef4\u7684\u57f9\u517b\u8fd8\u662f\u5f88\u6709\u5e2e\u52a9\u7684\u3002 EE16A&B: Designing Information Devices and Systems I&II \u662f\u4f2f\u514b\u5229 EE \u5b66\u751f\u7684\u5927\u4e00\u5165\u95e8\u8bfe\uff0c\u5176\u4e2d EE16A \u6ce8\u91cd\u901a\u8fc7\u7535\u8def\u4ece\u5b9e\u9645\u73af\u5883\u4e2d\u6536\u96c6\u548c\u5206\u6790\u6570\u636e\uff0c\u800c EE16B \u5219\u4fa7\u91cd\u4ece\u8fd9\u4e9b\u6536\u96c6\u5230\u7684\u6570\u636e\u8fdb\u884c\u5206\u6790\u5e76\u505a\u51fa\u9884\u6d4b\u884c\u4e3a\u3002 \u4fe1\u53f7\u4e0e\u7cfb\u7edf \u4fe1\u53f7\u4e0e\u7cfb\u7edf\u662f\u4e00\u95e8\u6211\u89c9\u5f97\u975e\u5e38\u503c\u5f97\u4e00\u4e0a\u7684\u8bfe\uff0c\u6700\u521d\u5b66\u5b83\u53ea\u662f\u4e3a\u4e86\u6ee1\u8db3\u6211\u5bf9\u5085\u91cc\u53f6\u53d8\u6362\u7684\u597d\u5947\uff0c\u4f46\u5b66\u5b8c\u4e4b\u540e\u6211\u624d\u4e0d\u7981\u611f\u53f9\uff0c\u5085\u7acb\u53f6\u53d8\u6362\u7ed9\u6211\u63d0\u4f9b\u4e86\u4e00\u4e2a\u5168\u65b0\u7684\u89c6\u89d2\u53bb\u770b\u5f85\u8fd9\u4e2a\u4e16\u754c\uff0c\u5c31\u5982\u540c\u5fae\u5206\u65b9\u7a0b\u4e00\u6837\uff0c\u8ba9\u4f60\u6c89\u6d78\u5728\u7528\u6570\u5b66\u53bb\u7cbe\u786e\u63cf\u7ed8\u548c\u523b\u753b\u8fd9\u4e2a\u4e16\u754c\u7684\u4f18\u96c5\u4e0e\u795e\u5947\u4e4b\u4e2d\u3002 MIT 6.003: signal and systems \u63d0\u4f9b\u4e86\u5168\u90e8\u7684\u8bfe\u7a0b\u5f55\u5f71\u3001\u4e66\u9762\u4f5c\u4e1a\u4ee5\u53ca\u7b54\u6848\u3002\u4e5f\u53ef\u4ee5\u53bb\u770b\u8fd9\u95e8\u8bfe\u7684 \u8fdc\u53e4\u7248\u672c \u800c UCB EE120: Signal and Systems \u5173\u4e8e\u5085\u7acb\u53f6\u53d8\u6362\u7684 notes \u5199\u5f97\u975e\u5e38\u597d\uff0c\u5e76\u4e14\u63d0\u4f9b\u4e866 \u4e2a\u975e\u5e38\u6709\u8da3\u7684 Python \u7f16\u7a0b\u4f5c\u4e1a\uff0c\u8ba9\u4f60\u5b9e\u8df5\u4e2d\u8fd0\u7528\u4fe1\u53f7\u4e0e\u7cfb\u7edf\u7684\u7406\u8bba\u4e0e\u7b97\u6cd5\u3002 \u6570\u636e\u7ed3\u6784\u4e0e\u7b97\u6cd5 \u6570\u636e\u7ed3\u6784\u4e0e\u7b97\u6cd5 UCB CS61B: Data Structures and Algorithms Coursera: Algorithms I & II \u7b97\u6cd5\u8bbe\u8ba1\u4e0e\u5206\u6790 UCB CS170: Efficient Algorithms and Intractable Problems \u8f6f\u4ef6\u5de5\u7a0b \u5165\u95e8\u8bfe \u4e00\u4efd\u201c\u80fd\u8dd1\u201d\u7684\u4ee3\u7801\uff0c\u548c\u4e00\u4efd\u9ad8\u8d28\u91cf\u7684\u5de5\u4e1a\u7ea7\u4ee3\u7801\u662f\u6709\u672c\u8d28\u533a\u522b\u7684\u3002\u56e0\u6b64\u6211\u975e\u5e38\u63a8\u8350\u4f4e\u5e74\u7ea7\u7684\u540c\u5b66\u5b66\u4e60\u4e00\u4e0b MIT 6.031: Software Construction \u8fd9\u95e8\u8bfe\uff0c\u5b83\u4f1a\u4ee5 Java \u8bed\u8a00\u4e3a\u57fa\u7840\uff0c\u4ee5\u4e30\u5bcc\u7ec6\u81f4\u7684\u9605\u8bfb\u6750\u6599\u548c\u7cbe\u5fc3\u8bbe\u8ba1\u7684\u7f16\u7a0b\u7ec3\u4e60\u4f20\u6388\u5982\u4f55\u7f16\u5199 \u4e0d\u6613\u51fa bug\u3001\u7b80\u660e\u6613\u61c2\u3001\u6613\u4e8e\u7ef4\u62a4\u4fee\u6539 \u7684\u9ad8\u8d28\u91cf\u4ee3\u7801\u3002\u5927\u5230\u5b8f\u89c2\u6570\u636e\u7ed3\u6784\u8bbe\u8ba1\uff0c\u5c0f\u5230\u5982\u4f55\u5199\u6ce8\u91ca\uff0c\u9075\u5faa\u8fd9\u4e9b\u524d\u4eba\u603b\u7ed3\u7684\u7ec6\u8282\u548c\u7ecf\u9a8c\uff0c\u5bf9\u4e8e\u4f60\u6b64\u540e\u7684\u7f16\u7a0b\u751f\u6daf\u5927\u6709\u88e8\u76ca\u3002 \u4e13\u4e1a\u8bfe \u5f53\u7136\uff0c\u5982\u679c\u4f60\u60f3\u7cfb\u7edf\u6027\u5730\u4e0a\u4e00\u95e8\u8f6f\u4ef6\u5de5\u7a0b\u7684\u8bfe\u7a0b\uff0c\u90a3\u6211\u63a8\u8350\u7684\u662f\u4f2f\u514b\u5229\u7684 UCB CS169: software engineering 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\u4e13\u4e1a\u8bfe \u5f53\u7136\uff0c\u5982\u679c\u60f3\u6df1\u5165\u73b0\u4ee3\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\u7684\u590d\u6742\u7ec6\u8282\uff0c\u8fd8\u5f97\u4e0a\u4e00\u95e8\u5927\u5b66\u672c\u79d1\u96be\u5ea6\u7684\u8bfe\u7a0b UCB CS61C: Great Ideas in Computer Architecture \u3002UC Berkeley \u4f5c\u4e3a RISC-V \u67b6\u6784\u7684\u53d1\u6e90\u5730\uff0c\u5728\u4f53\u7cfb\u7ed3\u6784\u9886\u57df\u7b97\u5f97\u4e0a\u9996\u5c48\u4e00\u6307\u3002\u5176\u8bfe\u7a0b\u975e\u5e38\u6ce8\u91cd\u5b9e\u8df5\uff0c\u4f60\u4f1a\u5728 Project \u4e2d\u624b\u5199\u6c47\u7f16\u6784\u9020\u795e\u7ecf\u7f51\u7edc\uff0c\u4ece\u96f6\u5f00\u59cb\u642d\u5efa\u4e00\u4e2a CPU\uff0c\u8fd9\u4e9b\u5b9e\u8df5\u90fd\u4f1a\u8ba9\u4f60\u5bf9\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\u6709\u66f4\u4e3a\u6df1\u5165\u7684\u7406\u89e3\uff0c\u800c\u4e0d\u662f\u4ec5\u505c\u7559\u4e8e\u201c\u53d6\u6307\u8bd1\u7801\u6267\u884c\u8bbf\u5b58\u5199\u56de\u201d\u7684\u5355\u8c03\u80cc\u8bf5\u91cc\u3002 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MIT6.033: System Engineering \u662f MIT \u7684\u7cfb\u7edf\u5165\u95e8\u8bfe\uff0c\u4e3b\u9898\u6d89\u53ca\u4e86\u64cd\u4f5c\u7cfb\u7edf\u3001\u7f51\u7edc\u3001\u5206\u5e03\u5f0f\u548c\u7cfb\u7edf\u5b89\u5168\uff0c\u9664\u4e86\u77e5\u8bc6\u70b9\u7684\u4f20\u6388\u5916\uff0c\u8fd9\u95e8\u8bfe\u8fd8\u4f1a\u8bb2\u6388\u4e00\u4e9b\u5199\u4f5c\u548c\u8868\u8fbe\u4e0a\u7684\u6280\u5de7\uff0c\u8ba9\u4f60\u5b66\u4f1a\u5982\u4f55\u8bbe\u8ba1\u5e76\u5411\u522b\u4eba\u4ecb\u7ecd\u548c\u5206\u6790\u81ea\u5df1\u7684\u7cfb\u7edf\u3002\u8fd9\u672c\u4e66\u914d\u5957\u7684\u6559\u6750 Principles of Computer System Design: An Introduction \u4e5f\u5199\u5f97\u975e\u5e38\u597d\uff0c\u63a8\u8350\u5927\u5bb6\u9605\u8bfb\u3002 CMU 15-213: Introduction to Computer System \u662f CMU 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\uff0c\u8fd9\u95e8\u8bfe\u7684\u7f16\u7a0b\u4f5c\u4e1a\u540c\u6837\u4f1a\u7528 Julia \u7f16\u7a0b\u8bed\u8a00\uff0c\u4e0d\u8fc7\u96be\u5ea6\u548c\u6df1\u5ea6\u4e0a\u90fd\u4e0a\u4e86\u4e00\u4e2a\u53f0\u9636\u3002\u5185\u5bb9\u6d89\u53ca\u4e86\u6d6e\u70b9\u7f16\u7801\u3001Root finding\u3001\u7ebf\u6027\u7cfb\u7edf\u3001\u5fae\u5206\u65b9\u7a0b\u7b49\u7b49\u65b9\u9762\uff0c\u6574\u95e8\u8bfe\u7684\u4e3b\u65e8\u5c31\u662f\u8ba9\u4f60\u5229\u7528\u79bb\u6563\u5316\u7684\u8ba1\u7b97\u673a\u8868\u793a\u53bb\u4f30\u8ba1\u548c\u903c\u8fd1\u4e00\u4e2a\u6570\u5b66\u4e0a\u8fde\u7eed\u7684\u6982\u5ff5\u3002\u8fd9\u95e8\u8bfe\u7684\u6559\u6388\u8fd8\u4e13\u95e8\u64b0\u5199\u4e86\u4e00\u672c\u914d\u5957\u7684\u5f00\u6e90\u6559\u6750 Fundamentals of Numerical Computation \uff0c\u91cc\u9762\u9644\u6709\u4e30\u5bcc\u7684 Julia \u4ee3\u7801\u5b9e\u4f8b\u548c\u4e25\u8c28\u7684\u516c\u5f0f\u63a8\u5bfc\u3002 \u5982\u679c\u4f60\u8fd8\u610f\u72b9\u672a\u5c3d\u7684\u8bdd\uff0c\u8fd8\u6709 MIT \u7684\u6570\u503c\u5206\u6790\u7814\u7a76\u751f\u8bfe\u7a0b 18.335: Introduction to numerical method \u4f9b\u4f60\u53c2\u8003\u3002","title":"\u6570\u503c\u5206\u6790"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_12","text":"\u5982\u679c\u4e16\u95f4\u4e07\u7269\u7684\u8fd0\u52a8\u53d1\u5c55\u90fd\u80fd\u7528\u65b9\u7a0b\u6765\u523b\u753b\u548c\u63cf\u8ff0\uff0c\u8fd9\u662f\u4e00\u4ef6\u591a\u4e48\u9177\u7684\u4e8b\u60c5\u5440\uff01\u867d\u7136\u51e0\u4e4e\u4efb\u4f55\u4e00\u6240\u5b66\u6821\u7684 CS \u57f9\u517b\u65b9\u6848\u4e2d\u90fd\u6ca1\u6709\u5fae\u5206\u65b9\u7a0b\u76f8\u5173\u7684\u5fc5\u4fee\u8bfe\u7a0b\uff0c\u4f46\u6211\u8fd8\u662f\u89c9\u5f97\u638c\u63e1\u5b83\u4f1a\u8d4b\u4e88\u4f60\u4e00\u4e2a\u65b0\u7684\u89c6\u89d2\u6765\u5ba1\u89c6\u8fd9\u4e2a\u4e16\u754c\u3002 \u7531\u4e8e\u5fae\u5206\u65b9\u7a0b\u4e2d\u5f80\u5f80\u4f1a\u7528\u5230\u5f88\u591a\u590d\u53d8\u51fd\u6570\u7684\u77e5\u8bc6\uff0c\u6240\u4ee5\u5927\u5bb6\u53ef\u4ee5\u53c2\u8003 MIT18.04: Complex variables functions \u7684\u8bfe\u7a0b notes \u6765\u8865\u9f50\u5148\u4fee\u77e5\u8bc6\u3002 MIT18.03: differential equations ) \u4e3b\u8981\u8986\u76d6\u4e86\u5e38\u5fae\u5206\u65b9\u7a0b\u7684\u6c42\u89e3\uff0c\u5728\u6b64\u57fa\u7840\u4e4b\u4e0a MIT18.152: Partial differential equations ) 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Since there's no universally perfect tool, there's no universally perfect language.","title":"\u7f16\u7a0b\u5165\u95e8"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#shell","text":"MIT-Missing-Semester","title":"Shell"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#python","text":"Harvard CS50: This is CS50x UCB CS61A: Structure and Interpretation of Computer Programs","title":"Python"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#c","text":"Stanford CS106B/X: Programming Abstractions Stanford CS106L: Standard C++ Programming","title":"C++"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#rust","text":"Stanford CS110L: Safety in Systems Programming","title":"Rust"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#ocaml","text":"Cornell CS3110 textbook: Functional Programming in OCaml","title":"OCaml"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_20","text":"","title":"\u7535\u5b50\u57fa\u7840"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_21","text":"\u4f5c\u4e3a\u8ba1\u7b97\u673a\u7cfb\u7684\u5b66\u751f\uff0c\u4e86\u89e3\u4e00\u4e9b\u57fa\u7840\u7684\u7535\u8def\u77e5\u8bc6\uff0c\u611f\u53d7\u4ece\u4f20\u611f\u5668\u6536\u96c6\u6570\u636e\u5230\u6570\u636e\u5206\u6790\u518d\u5230\u7b97\u6cd5\u9884\u6d4b\u6574\u6761\u6d41\u6c34\u7ebf\uff0c\u5bf9\u4e8e\u540e\u7eed\u77e5\u8bc6\u7684\u5b66\u4e60\u4ee5\u53ca\u8ba1\u7b97\u601d\u7ef4\u7684\u57f9\u517b\u8fd8\u662f\u5f88\u6709\u5e2e\u52a9\u7684\u3002 EE16A&B: Designing Information Devices and Systems I&II \u662f\u4f2f\u514b\u5229 EE \u5b66\u751f\u7684\u5927\u4e00\u5165\u95e8\u8bfe\uff0c\u5176\u4e2d EE16A \u6ce8\u91cd\u901a\u8fc7\u7535\u8def\u4ece\u5b9e\u9645\u73af\u5883\u4e2d\u6536\u96c6\u548c\u5206\u6790\u6570\u636e\uff0c\u800c EE16B \u5219\u4fa7\u91cd\u4ece\u8fd9\u4e9b\u6536\u96c6\u5230\u7684\u6570\u636e\u8fdb\u884c\u5206\u6790\u5e76\u505a\u51fa\u9884\u6d4b\u884c\u4e3a\u3002","title":"\u7535\u8def\u57fa\u7840"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_22","text":"\u4fe1\u53f7\u4e0e\u7cfb\u7edf\u662f\u4e00\u95e8\u6211\u89c9\u5f97\u975e\u5e38\u503c\u5f97\u4e00\u4e0a\u7684\u8bfe\uff0c\u6700\u521d\u5b66\u5b83\u53ea\u662f\u4e3a\u4e86\u6ee1\u8db3\u6211\u5bf9\u5085\u91cc\u53f6\u53d8\u6362\u7684\u597d\u5947\uff0c\u4f46\u5b66\u5b8c\u4e4b\u540e\u6211\u624d\u4e0d\u7981\u611f\u53f9\uff0c\u5085\u7acb\u53f6\u53d8\u6362\u7ed9\u6211\u63d0\u4f9b\u4e86\u4e00\u4e2a\u5168\u65b0\u7684\u89c6\u89d2\u53bb\u770b\u5f85\u8fd9\u4e2a\u4e16\u754c\uff0c\u5c31\u5982\u540c\u5fae\u5206\u65b9\u7a0b\u4e00\u6837\uff0c\u8ba9\u4f60\u6c89\u6d78\u5728\u7528\u6570\u5b66\u53bb\u7cbe\u786e\u63cf\u7ed8\u548c\u523b\u753b\u8fd9\u4e2a\u4e16\u754c\u7684\u4f18\u96c5\u4e0e\u795e\u5947\u4e4b\u4e2d\u3002 MIT 6.003: signal and systems 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Algorithms and Intractable Problems","title":"\u7b97\u6cd5\u8bbe\u8ba1\u4e0e\u5206\u6790"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_26","text":"","title":"\u8f6f\u4ef6\u5de5\u7a0b"},{"location":"en/CS%E5%AD%A6%E4%B9%A0%E8%A7%84%E5%88%92/#_27","text":"\u4e00\u4efd\u201c\u80fd\u8dd1\u201d\u7684\u4ee3\u7801\uff0c\u548c\u4e00\u4efd\u9ad8\u8d28\u91cf\u7684\u5de5\u4e1a\u7ea7\u4ee3\u7801\u662f\u6709\u672c\u8d28\u533a\u522b\u7684\u3002\u56e0\u6b64\u6211\u975e\u5e38\u63a8\u8350\u4f4e\u5e74\u7ea7\u7684\u540c\u5b66\u5b66\u4e60\u4e00\u4e0b MIT 6.031: Software Construction \u8fd9\u95e8\u8bfe\uff0c\u5b83\u4f1a\u4ee5 Java \u8bed\u8a00\u4e3a\u57fa\u7840\uff0c\u4ee5\u4e30\u5bcc\u7ec6\u81f4\u7684\u9605\u8bfb\u6750\u6599\u548c\u7cbe\u5fc3\u8bbe\u8ba1\u7684\u7f16\u7a0b\u7ec3\u4e60\u4f20\u6388\u5982\u4f55\u7f16\u5199 \u4e0d\u6613\u51fa bug\u3001\u7b80\u660e\u6613\u61c2\u3001\u6613\u4e8e\u7ef4\u62a4\u4fee\u6539 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2021\u5e7412\u670812\u65e5\u5199\u4e8e\u71d5\u56ed","title":"\u540e\u8bb0"},{"location":"en/%E5%9F%B9%E5%85%BB%E6%96%B9%E6%A1%88Pro/","text":"under construction.","title":"\u57f9\u517b\u65b9\u6848Pro"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/","text":"\u597d\u4e66\u63a8\u8350 \u7531\u4e8e\u7248\u6743\u539f\u56e0\uff0c\u4e0b\u9762\u5217\u4e3e\u7684\u56fe\u4e66\u4e2d\u9664\u4e86\u5f00\u6e90\u8d44\u6e90\u63d0\u4f9b\u4e86\u94fe\u63a5\uff0c\u5176\u4ed6\u7684\u8d44\u6e90\u8bf7\u5927\u5bb6\u81ea\u884c\u901a\u8fc7 libgen \u6216 z-lib \u67e5\u627e\u3002 \u8d44\u6e90\u6c47\u603b Free Programming Books : \u5f00\u6e90\u7f16\u7a0b\u4e66\u7c4d\u8d44\u6e90\u6c47\u603b CS Textbook Recommendations : \u8ba1\u7b97\u673a\u79d1\u5b66\u65b9\u5411\u63a8\u8350\u6559\u6750\u5217\u8868 C Book Guide and List : C\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 C++ Book Guide and List : C++\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 Python Book Guide and List : Python\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 \u7cfb\u7edf\u5165\u95e8 Computer Systems: A Programmer's Perspective [ \u8c46\u74e3 ] Principles of Computer System Design: An Introduction [ \u8c46\u74e3 ] \u64cd\u4f5c\u7cfb\u7edf \u73b0\u4ee3\u64cd\u4f5c\u7cfb\u7edf: \u539f\u7406\u4e0e\u5b9e\u73b0 [ \u8c46\u74e3 ] Operating Systems: Three Easy Pieces [ \u8c46\u74e3 ] Modern Operating Systems [ \u8c46\u74e3 ] Operating Systems: Principles and Practice [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u7f51\u7edc Computer Networks: A Systems Approach [ \u8c46\u74e3 ] Computer Networking: A Top-Down Approach [ \u8c46\u74e3 ] \u5206\u5e03\u5f0f\u7cfb\u7edf Patterns of Distributed System (Blog) Distributed Systems for Fun and Profit (Blog) Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems [ \u8c46\u74e3 ] \u6570\u636e\u5e93\u7cfb\u7edf Architecture of a Database System [ \u8c46\u74e3 ] Readings in Database Systems [ \u8c46\u74e3 ] Database System Concepts [ \u8c46\u74e3 ] \u7f16\u8bd1\u539f\u7406 Engineering a Compiler [ \u8c46\u74e3 ] Compilers: Principles, Techniques, and Tools [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u7f16\u7a0b\u8bed\u8a00 \u8ba1\u7b97\u673a\u7a0b\u5e8f\u7684\u6784\u9020\u548c\u89e3\u91ca [ \u8c46\u74e3 ] Essentials of Programming Languages [ \u8c46\u74e3 ] Practical Foundations for Programming Languages [ \u8c46\u74e3 ] Software Foundations [ \u8c46\u74e3 ] [ \u5317\u5927\u76f8\u5173\u8bfe\u7a0b ] Types and Programming Languages [ \u8c46\u74e3 ] [ \u5317\u5927\u76f8\u5173\u8bfe\u7a0b ] \u4f53\u7cfb\u7ed3\u6784 \u8d85\u6807\u91cf\u5904\u7406\u5668\u8bbe\u8ba1: Superscalar RISC Processor Design [ \u8c46\u74e3 ] Computer Organization and Design RISC-V Edition [ \u8c46\u74e3 ] Computer Organization and Design: The Hardware/Software Interface [ \u8c46\u74e3 ] Computer Architecture: A Quantitative Approach [ \u8c46\u74e3 ] \u7406\u8bba\u8ba1\u7b97\u673a\u79d1\u5b66 Introduction to the Theory of Computation [ \u8c46\u74e3 ] \u5bc6\u7801\u5b66 Cryptography Engineering: Design Principles and Practical Applications [ \u8c46\u74e3 ] Introduction to Modern Cryptography [ \u8c46\u74e3 ] \u9006\u5411\u5de5\u7a0b \u9006\u5411\u5de5\u7a0b\u6838\u5fc3\u539f\u7406 [ \u8c46\u74e3 ] \u52a0\u5bc6\u4e0e\u89e3\u5bc6 [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u56fe\u5f62\u5b66 Monte Carlo theory, methods and examples Advanced Global Illumination [ \u8c46\u74e3 ] Fundamentals of Computer Graphics [ \u8c46\u74e3 ] Fluid Simulation for Computer Graphics [ \u8c46\u74e3 ] Physically Based Rendering: From Theory To Implementation [ \u8c46\u74e3 ] Real-Time Rendering [ \u8c46\u74e3 ] \u6e38\u620f\u5f15\u64ce \u6e38\u620f\u7f16\u7a0b\u6a21\u5f0f: Game Programming Patterns [ \u8c46\u74e3 ] \u5b9e\u65f6\u78b0\u649e\u68c0\u6d4b\u7b97\u6cd5\u6280\u672f [ \u8c46\u74e3 ] Game AI Pro Series [ \u8c46\u74e3 ] Artificial Intelligence for Games [ \u8c46\u74e3 ] Game Engine Architecture [ \u8c46\u74e3 ] Game Programming Gems Series [ \u8c46\u74e3 ] \u8f6f\u4ef6\u5de5\u7a0b Software Engineering at Google [ \u8c46\u74e3 ] \u8bbe\u8ba1\u6a21\u5f0f \u8bbe\u8ba1\u6a21\u5f0f: \u53ef\u590d\u7528\u9762\u5411\u5bf9\u8c61\u8f6f\u4ef6\u7684\u57fa\u7840 [ \u8c46\u74e3 ] \u5927\u8bdd\u8bbe\u8ba1\u6a21\u5f0f [ \u8c46\u74e3 ] Head First \u8bbe\u8ba1\u6a21\u5f0f [ \u8c46\u74e3 ] \u6df1\u5ea6\u5b66\u4e60 \u52a8\u624b\u5b66\u6df1\u5ea6\u5b66\u4e60 [ \u8c46\u74e3 ] \u795e\u7ecf\u7f51\u7edc\u4e0e\u6df1\u5ea6\u5b66\u4e60 [ \u8c46\u74e3 ] \u6df1\u5ea6\u5b66\u4e60\u5165\u95e8 [ \u8c46\u74e3 ] \u7b80\u5355\u7c97\u66b4 TensorFlow 2 (Tutorial) Speech and Language Processing [ \u8c46\u74e3 ] \u8ba1\u7b97\u673a\u89c6\u89c9 Multiple View Geometry in Computer Vision [ \u8c46\u74e3 ] \u673a\u5668\u4eba Probabilistic Robotics [ \u8c46\u74e3 ] \u9762\u8bd5 \u5251\u6307 Offer\uff1a\u540d\u4f01\u9762\u8bd5\u5b98\u7cbe\u8bb2\u5178\u578b\u7f16\u7a0b\u9898 [ \u8c46\u74e3 ] Cracking The Coding Interview [ \u8c46\u74e3 ]","title":"Book Recommendation"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_1","text":"\u7531\u4e8e\u7248\u6743\u539f\u56e0\uff0c\u4e0b\u9762\u5217\u4e3e\u7684\u56fe\u4e66\u4e2d\u9664\u4e86\u5f00\u6e90\u8d44\u6e90\u63d0\u4f9b\u4e86\u94fe\u63a5\uff0c\u5176\u4ed6\u7684\u8d44\u6e90\u8bf7\u5927\u5bb6\u81ea\u884c\u901a\u8fc7 libgen \u6216 z-lib \u67e5\u627e\u3002","title":"\u597d\u4e66\u63a8\u8350"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_2","text":"Free Programming Books : \u5f00\u6e90\u7f16\u7a0b\u4e66\u7c4d\u8d44\u6e90\u6c47\u603b CS Textbook Recommendations : \u8ba1\u7b97\u673a\u79d1\u5b66\u65b9\u5411\u63a8\u8350\u6559\u6750\u5217\u8868 C Book Guide and List : C\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 C++ Book Guide and List : C++\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868 Python Book Guide and List : Python\u8bed\u8a00\u76f8\u5173\u7684\u7f16\u7a0b\u4e66\u7c4d\u63a8\u8350\u5217\u8868","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_3","text":"Computer Systems: A Programmer's Perspective [ \u8c46\u74e3 ] Principles of Computer System Design: An Introduction [ \u8c46\u74e3 ]","title":"\u7cfb\u7edf\u5165\u95e8"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_4","text":"\u73b0\u4ee3\u64cd\u4f5c\u7cfb\u7edf: \u539f\u7406\u4e0e\u5b9e\u73b0 [ \u8c46\u74e3 ] Operating Systems: Three Easy Pieces [ \u8c46\u74e3 ] Modern Operating Systems [ \u8c46\u74e3 ] Operating Systems: Principles and Practice [ \u8c46\u74e3 ]","title":"\u64cd\u4f5c\u7cfb\u7edf"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_5","text":"Computer Networks: A Systems Approach [ \u8c46\u74e3 ] Computer Networking: A Top-Down Approach [ \u8c46\u74e3 ]","title":"\u8ba1\u7b97\u673a\u7f51\u7edc"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_6","text":"Patterns of Distributed System (Blog) Distributed Systems for Fun and Profit (Blog) Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems [ \u8c46\u74e3 ]","title":"\u5206\u5e03\u5f0f\u7cfb\u7edf"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_7","text":"Architecture of a Database System [ \u8c46\u74e3 ] Readings in Database Systems [ \u8c46\u74e3 ] Database System Concepts [ \u8c46\u74e3 ]","title":"\u6570\u636e\u5e93\u7cfb\u7edf"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_8","text":"Engineering a Compiler [ \u8c46\u74e3 ] Compilers: Principles, Techniques, and Tools [ \u8c46\u74e3 ]","title":"\u7f16\u8bd1\u539f\u7406"},{"location":"en/%E5%A5%BD%E4%B9%A6%E6%8E%A8%E8%8D%90/#_9","text":"\u8ba1\u7b97\u673a\u7a0b\u5e8f\u7684\u6784\u9020\u548c\u89e3\u91ca [ \u8c46\u74e3 ] Essentials of Programming Languages [ \u8c46\u74e3 ] Practical Foundations for Programming Languages [ \u8c46\u74e3 ] Software Foundations [ \u8c46\u74e3 ] [ 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\u73af\u5883\u51fa\u73b0\u4e86\u95ee\u9898\uff0c\u53ef\u4ee5\u8003\u8651\u4f7f\u7528 Overleaf \u8fd9\u4e2a\u5728\u7ebf LaTeX \u7f16\u8f91\u7f51\u7ad9\u3002\u7ad9\u5185\u4e0d\u4ec5\u6709\u5404\u79cd\u5404\u6837\u7684 LaTeX \u6a21\u7248\u4f9b\u4f60\u9009\u62e9\uff0c\u8fd8\u514d\u53bb\u4e86\u73af\u5883\u914d\u7f6e\u7684\u96be\u9898\u3002 \u9605\u8bfb\u4e0b\u9762\u4e09\u7bc7 Tutorial: Part-1 , Part-2 , Part-3 \u3002 \u5b66\u4e60 LaTeX \u6700\u597d\u7684\u65b9\u5f0f\u5f53\u7136\u662f\u5199\u8bba\u6587\uff0c\u4e0d\u8fc7\u4ece\u4e00\u95e8\u6570\u5b66\u8bfe\u5165\u624b\u7528 LaTeX \u5199\u4f5c\u4e1a\u4e5f\u662f\u4e00\u4e2a\u4e0d\u9519\u7684\u9009\u62e9\u3002","title":"\u5982\u4f55\u5b66\u4e60 LaTeX"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/","text":"Vim \u4e3a\u4ec0\u4e48\u5b66\u4e60 Vim \u5728\u6211\u770b\u6765 Vim \u7f16\u8f91\u5668\u6709\u5982\u4e0b\u7684\u597d\u5904\uff1a 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\u7684\u5b66\u4e60\u8d44\u6599\u6d69\u5982\u70df\u6d77\uff0c\u4f46\u638c\u63e1\u5b83\u6700\u597d\u7684\u65b9\u5f0f\u8fd8\u662f\u5c06\u5b83\u7528\u5728\u65e5\u5e38\u7684\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u800c\u4e0d\u662f\u4e00\u4e0a\u6765\u5c31\u53bb\u5b66\u5404\u79cd\u82b1\u91cc\u80e1\u54e8\u7684\u9ad8\u7ea7 Vim \u6280\u5de7\u3002\u4e2a\u4eba\u63a8\u8350\u7684\u5b66\u4e60\u8def\u7ebf\u5982\u4e0b\uff1a \u5148\u9605\u8bfb \u8fd9\u7bc7 tutorial \uff0c\u638c\u63e1\u57fa\u672c\u7684 Vim \u6982\u5ff5\u548c\u4f7f\u7528\u65b9\u5f0f\u3002 \u7528 Vim \u81ea\u5e26\u7684 vimtutor \u8fdb\u884c\u7ec3\u4e60\uff0c\u5b89\u88c5\u5b8c Vim \u4e4b\u540e\u76f4\u63a5\u5728\u547d\u4ee4\u884c\u91cc\u8f93\u5165 vimtutor \u5373\u53ef\u8fdb\u5165\u7ec3\u4e60\u7a0b\u5e8f\u3002 \u6700\u540e\u5c31\u662f\u5f3a\u8feb\u81ea\u5df1\u4f7f\u7528 Vim \u8fdb\u884c\u5f00\u53d1\uff0cIDE \u91cc\u53ef\u4ee5\u5b89\u88c5 Vim \u63d2\u4ef6\u3002 \u7b49\u4f60\u5b8c\u5168\u9002\u5e94 Vim \u4e4b\u540e\u65b0\u7684\u4e16\u754c\u4fbf\u5411\u4f60\u655e\u5f00\u4e86\u5927\u95e8\uff0c\u4f60\u53ef\u4ee5\u6309\u9700\u914d\u7f6e\u81ea\u5df1\u7684 Vim\uff08\u4fee\u6539 .vimrc \u6587\u4ef6\uff09\uff0c\u7f51\u4e0a\u6709\u6570\u4e0d\u80dc\u6570\u7684\u8d44\u6e90\u53ef\u4ee5\u501f\u9274\u3002 \u63a8\u8350\u53c2\u8003\u8d44\u6599 Neil, Drew. Practical Vim: Edit Text at the Speed of Thought. N.p., Pragmatic Bookshelf, 2015. Neil, Drew. Modern Vim: Craft Your Development Environment with Vim 8 and Neovim. United States, Pragmatic Bookshelf.","title":"Vim"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#vim","text":"","title":"Vim"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#vim_1","text":"\u5728\u6211\u770b\u6765 Vim \u7f16\u8f91\u5668\u6709\u5982\u4e0b\u7684\u597d\u5904\uff1a \u8ba9\u4f60\u7684\u6574\u4e2a\u5f00\u53d1\u8fc7\u7a0b\u624b\u6307\u4e0d\u9700\u8981\u79bb\u5f00\u952e\u76d8\uff0c\u800c\u4e14\u5149\u6807\u7684\u79fb\u52a8\u4e0d\u9700\u8981\u65b9\u5411\u952e\u4f7f\u5f97\u4f60\u7684\u624b\u6307\u4e00\u76f4\u5904\u5728\u6253\u5b57\u7684\u6700\u4f73\u4f4d\u7f6e\u3002 \u65b9\u4fbf\u7684\u6587\u4ef6\u5207\u6362\u4ee5\u53ca\u9762\u677f\u63a7\u5236\u53ef\u4ee5\u8ba9\u4f60\u540c\u65f6\u5f00\u53d1\u591a\u4efd\u6587\u4ef6\u751a\u81f3\u540c\u4e00\u4e2a\u6587\u4ef6\u7684\u4e0d\u540c\u4f4d\u7f6e\u3002 Vim \u7684\u5b8f\u64cd\u4f5c\u53ef\u4ee5\u6279\u91cf\u5316\u5904\u7406\u91cd\u590d\u64cd\u4f5c\uff08\u4f8b\u5982\u591a\u884c tab\uff0c\u6279\u91cf\u52a0\u53cc\u5f15\u53f7\u7b49\u7b49\uff09 Vim \u662f\u5f88\u591a\u670d\u52a1\u5668\u81ea\u5e26\u7684\u547d\u4ee4\u884c\u7f16\u8f91\u5668\uff0c\u5f53\u4f60\u901a\u8fc7 ssh \u8fde\u63a5\u8fdc\u7a0b\u670d\u52a1\u5668\u4e4b\u540e\uff0c\u7531\u4e8e\u6ca1\u6709\u56fe\u5f62\u754c\u9762\uff0c\u53ea\u80fd\u5728\u547d\u4ee4\u884c\u91cc\u8fdb\u884c\u5f00\u53d1\uff08\u5f53\u7136\u73b0\u5728\u5f88\u591a IDE \u5982 VS Code \u63d0\u4f9b\u4e86 ssh \u63d2\u4ef6\u53ef\u4ee5\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff09\u3002 \u5f02\u5e38\u4e30\u5bcc\u7684\u63d2\u4ef6\u751f\u6001\uff0c\u8ba9\u4f60\u62e5\u6709\u4e16\u754c\u4e0a\u6700\u82b1\u91cc\u80e1\u54e8\u7684\u547d\u4ee4\u884c\u7f16\u8f91\u5668\u3002","title":"\u4e3a\u4ec0\u4e48\u5b66\u4e60 Vim"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#vim_2","text":"\u4e0d\u5e78\u7684\u662f Vim \u7684\u5b66\u4e60\u66f2\u7ebf\u786e\u5b9e\u76f8\u5f53\u9661\u5ced\uff0c\u6211\u82b1\u4e86\u597d\u51e0\u4e2a\u661f\u671f\u624d\u6162\u6162\u9002\u5e94\u4e86\u7528 Vim \u8fdb\u884c\u5f00\u53d1\u7684\u8fc7\u7a0b\u3002\u6700\u5f00\u59cb\u4f60\u4f1a\u89c9\u5f97\u975e\u5e38\u4e0d\u9002\u5e94\uff0c\u4f46\u4e00\u65e6\u71ac\u8fc7\u4e86\u521d\u59cb\u9636\u6bb5\uff0c\u76f8\u4fe1\u6211\uff0c\u4f60\u4f1a\u7231\u4e0a Vim\u3002 Vim \u7684\u5b66\u4e60\u8d44\u6599\u6d69\u5982\u70df\u6d77\uff0c\u4f46\u638c\u63e1\u5b83\u6700\u597d\u7684\u65b9\u5f0f\u8fd8\u662f\u5c06\u5b83\u7528\u5728\u65e5\u5e38\u7684\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u800c\u4e0d\u662f\u4e00\u4e0a\u6765\u5c31\u53bb\u5b66\u5404\u79cd\u82b1\u91cc\u80e1\u54e8\u7684\u9ad8\u7ea7 Vim \u6280\u5de7\u3002\u4e2a\u4eba\u63a8\u8350\u7684\u5b66\u4e60\u8def\u7ebf\u5982\u4e0b\uff1a \u5148\u9605\u8bfb \u8fd9\u7bc7 tutorial \uff0c\u638c\u63e1\u57fa\u672c\u7684 Vim \u6982\u5ff5\u548c\u4f7f\u7528\u65b9\u5f0f\u3002 \u7528 Vim \u81ea\u5e26\u7684 vimtutor \u8fdb\u884c\u7ec3\u4e60\uff0c\u5b89\u88c5\u5b8c Vim \u4e4b\u540e\u76f4\u63a5\u5728\u547d\u4ee4\u884c\u91cc\u8f93\u5165 vimtutor \u5373\u53ef\u8fdb\u5165\u7ec3\u4e60\u7a0b\u5e8f\u3002 \u6700\u540e\u5c31\u662f\u5f3a\u8feb\u81ea\u5df1\u4f7f\u7528 Vim \u8fdb\u884c\u5f00\u53d1\uff0cIDE \u91cc\u53ef\u4ee5\u5b89\u88c5 Vim \u63d2\u4ef6\u3002 \u7b49\u4f60\u5b8c\u5168\u9002\u5e94 Vim \u4e4b\u540e\u65b0\u7684\u4e16\u754c\u4fbf\u5411\u4f60\u655e\u5f00\u4e86\u5927\u95e8\uff0c\u4f60\u53ef\u4ee5\u6309\u9700\u914d\u7f6e\u81ea\u5df1\u7684 Vim\uff08\u4fee\u6539 .vimrc \u6587\u4ef6\uff09\uff0c\u7f51\u4e0a\u6709\u6570\u4e0d\u80dc\u6570\u7684\u8d44\u6e90\u53ef\u4ee5\u501f\u9274\u3002","title":"\u5982\u4f55\u5b66\u4e60 Vim"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/Vim/#_1","text":"Neil, Drew. Practical Vim: Edit Text at the Speed of Thought. N.p., Pragmatic Bookshelf, 2015. Neil, Drew. Modern Vim: Craft Your Development Environment with Vim 8 and Neovim. United States, Pragmatic Bookshelf.","title":"\u63a8\u8350\u53c2\u8003\u8d44\u6599"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/thesis/","text":"\u6bd5\u4e1a\u8bba\u6587 \u4e3a\u4ec0\u4e48\u5199\u8fd9\u4efd\u6559\u7a0b 2022\u5e74\uff0c\u6211\u672c\u79d1\u6bd5\u4e1a\u4e86\u3002\u5728\u5f00\u59cb\u52a8\u624b\u5199\u6bd5\u4e1a\u8bba\u6587\u7684\u65f6\u5019\uff0c\u6211\u5c34\u5c2c\u5730\u53d1\u73b0\uff0c\u6211\u5bf9 Word \u7684\u638c\u63e1\u7a0b\u5ea6\u4ec5\u9650\u4e8e\u8c03\u8282\u5b57\u4f53\u3001\u4fdd\u5b58\u5bfc\u51fa\u8fd9\u4e9b\u50bb\u74dc\u529f\u80fd\u3002\u66fe\u60f3\u8f6c\u6218 Latex\uff0c\u4f46\u8bba\u6587\u7684\u6bb5\u843d\u683c\u5f0f\u8981\u6c42\u8c03\u6574\u8d77\u6765\u8fd8\u662f\u7528 Word 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\u6cb9\u7ba1\u89c6\u9891\u4e0b\u8f7d\u5668\u3002 zlibrary : \u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9(\u53ef\u80fd\u9700\u8981\u7ffb\u5899)\u3002 \u8bbe\u8ba1\u5de5\u5177 excalidraw : \u4e00\u6b3e\u624b\u7ed8\u98ce\u683c\u7684\u7ed8\u56fe\u5de5\u5177\uff0c\u975e\u5e38\u9002\u5408\u7ed8\u5236\u8bfe\u7a0b\u62a5\u544a\u6216\u8005PPT\u5185\u7684\u793a\u610f\u56fe\u3002 origamiway : \u624b\u628a\u624b\u6559\u4f60\u600e\u4e48\u6298\u7eb8\u3002 thingiverse : \u56ca\u62ec\u5404\u7c7b 2D/3D \u8bbe\u8ba1\u8d44\u6e90\uff0c\u5176 STL \u6587\u4ef6\u4e0b\u8f7d\u53ef\u76f4\u63a5 3D \u6253\u5370\u3002 iconfont : \u56fd\u5185\u6700\u5927\u7684\u56fe\u6807\u548c\u63d2\u753b\u8d44\u6e90\u5e93\uff0c\u53ef\u7528\u4e8e\u5f00\u53d1\u6216\u7ed8\u5236\u7cfb\u7edf\u67b6\u6784\u56fe\u3002 turbosquid : \u53ef\u4ee5\u8d2d\u4e70\u5404\u5f0f\u5404\u6837\u7684\u6a21\u578b\u3002 \u7f16\u7a0b\u76f8\u5173 sqlfiddle : \u4e00\u4e2a\u7b80\u6613\u7684\u5728\u7ebf SQL Playground\u3002 godbolt : \u975e\u5e38\u65b9\u4fbf\u7684\u7f16\u8bd1\u5668\u63a2\u7d22\u5de5\u5177\u3002\u4f60\u53ef\u4ee5\u5199\u4e00\u6bb5 C/C++ \u4ee3\u7801\uff0c\u9009\u62e9\u4e00\u6b3e\u7f16\u8bd1\u5668\uff0c\u7136\u540e\u4fbf\u53ef\u4ee5\u89c2\u5bdf\u751f\u6210\u7684\u5177\u4f53\u6c47\u7f16\u4ee3\u7801\u3002 explainshell : \u4f60\u662f\u5426\u66fe\u4e3a\u4e00\u6bb5 shell \u4ee3\u7801\u7684\u5177\u4f53\u542b\u4e49\u611f\u5230\u56f0\u6270\uff1fmanpage \u770b\u534a\u5929\u8fd8\u662f\u4e0d\u660e\u6240\u4ee5\uff1f\u8bd5\u8bd5\u8fd9\u4e2a\u7f51\u7ad9\uff01 regex101 : \u6b63\u5219\u8868\u8fbe\u5f0f\u8c03\u8bd5\u7f51\u7ad9\uff0c\u652f\u6301\u5404\u79cd\u7f16\u7a0b\u8bed\u8a00\u7684\u5339\u914d\u6807\u51c6\u3002 typingtom : \u9488\u5bf9\u7a0b\u5e8f\u5458\u7684\u6253\u5b57\u7ec3\u4e60/\u6d4b\u901f\u7f51\u7ad9\u3002 \u5b66\u4e60\u7f51\u7ad9 HFS : \u5404\u7c7b\u8f6f\u4ef6\u6559\u7a0b\u3002 os-wiki : \u64cd\u4f5c\u7cfb\u7edf\u6280\u672f\u8d44\u6e90\u767e\u79d1\u5168\u4e66\u3002 Shadertoy : \u7f16\u5199\u5404\u5f0f\u5404\u6837\u7684 shader\u3002 \u6742\u9879 tophub : \u65b0\u95fb\u70ed\u699c\u5408\u96c6\uff08\u7efc\u5408\u4e86\u77e5\u4e4e\u3001\u5fae\u535a\u3001\u767e\u5ea6\u3001\u5fae\u4fe1\u7b49\uff09\u3002 speedtest : \u5728\u7ebf\u7f51\u7edc\u6d4b\u901f\u7f51\u7ad9\u3002 public-apis : \u516c\u5171 API \u5408\u96c6\u5217\u8868\u3002","title":"\u5b9e\u7528\u5de5\u5177\u7bb1"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_1","text":"","title":"\u5b9e\u7528\u5de5\u5177\u7bb1"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_2","text":"Libgen : PDF\u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9\u3002 z-epub : ePub\u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9\u3002 bitdownloader : \u6cb9\u7ba1\u89c6\u9891\u4e0b\u8f7d\u5668\u3002 zlibrary : \u7535\u5b50\u4e66\u4e0b\u8f7d\u7f51\u7ad9(\u53ef\u80fd\u9700\u8981\u7ffb\u5899)\u3002","title":"\u4e0b\u8f7d\u5de5\u5177"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_3","text":"excalidraw : \u4e00\u6b3e\u624b\u7ed8\u98ce\u683c\u7684\u7ed8\u56fe\u5de5\u5177\uff0c\u975e\u5e38\u9002\u5408\u7ed8\u5236\u8bfe\u7a0b\u62a5\u544a\u6216\u8005PPT\u5185\u7684\u793a\u610f\u56fe\u3002 origamiway : \u624b\u628a\u624b\u6559\u4f60\u600e\u4e48\u6298\u7eb8\u3002 thingiverse : \u56ca\u62ec\u5404\u7c7b 2D/3D \u8bbe\u8ba1\u8d44\u6e90\uff0c\u5176 STL \u6587\u4ef6\u4e0b\u8f7d\u53ef\u76f4\u63a5 3D \u6253\u5370\u3002 iconfont : \u56fd\u5185\u6700\u5927\u7684\u56fe\u6807\u548c\u63d2\u753b\u8d44\u6e90\u5e93\uff0c\u53ef\u7528\u4e8e\u5f00\u53d1\u6216\u7ed8\u5236\u7cfb\u7edf\u67b6\u6784\u56fe\u3002 turbosquid : \u53ef\u4ee5\u8d2d\u4e70\u5404\u5f0f\u5404\u6837\u7684\u6a21\u578b\u3002","title":"\u8bbe\u8ba1\u5de5\u5177"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_4","text":"sqlfiddle : \u4e00\u4e2a\u7b80\u6613\u7684\u5728\u7ebf SQL Playground\u3002 godbolt : \u975e\u5e38\u65b9\u4fbf\u7684\u7f16\u8bd1\u5668\u63a2\u7d22\u5de5\u5177\u3002\u4f60\u53ef\u4ee5\u5199\u4e00\u6bb5 C/C++ \u4ee3\u7801\uff0c\u9009\u62e9\u4e00\u6b3e\u7f16\u8bd1\u5668\uff0c\u7136\u540e\u4fbf\u53ef\u4ee5\u89c2\u5bdf\u751f\u6210\u7684\u5177\u4f53\u6c47\u7f16\u4ee3\u7801\u3002 explainshell : \u4f60\u662f\u5426\u66fe\u4e3a\u4e00\u6bb5 shell \u4ee3\u7801\u7684\u5177\u4f53\u542b\u4e49\u611f\u5230\u56f0\u6270\uff1fmanpage \u770b\u534a\u5929\u8fd8\u662f\u4e0d\u660e\u6240\u4ee5\uff1f\u8bd5\u8bd5\u8fd9\u4e2a\u7f51\u7ad9\uff01 regex101 : \u6b63\u5219\u8868\u8fbe\u5f0f\u8c03\u8bd5\u7f51\u7ad9\uff0c\u652f\u6301\u5404\u79cd\u7f16\u7a0b\u8bed\u8a00\u7684\u5339\u914d\u6807\u51c6\u3002 typingtom : \u9488\u5bf9\u7a0b\u5e8f\u5458\u7684\u6253\u5b57\u7ec3\u4e60/\u6d4b\u901f\u7f51\u7ad9\u3002","title":"\u7f16\u7a0b\u76f8\u5173"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_5","text":"HFS : \u5404\u7c7b\u8f6f\u4ef6\u6559\u7a0b\u3002 os-wiki : \u64cd\u4f5c\u7cfb\u7edf\u6280\u672f\u8d44\u6e90\u767e\u79d1\u5168\u4e66\u3002 Shadertoy : \u7f16\u5199\u5404\u5f0f\u5404\u6837\u7684 shader\u3002","title":"\u5b66\u4e60\u7f51\u7ad9"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/tools/#_6","text":"tophub : \u65b0\u95fb\u70ed\u699c\u5408\u96c6\uff08\u7efc\u5408\u4e86\u77e5\u4e4e\u3001\u5fae\u535a\u3001\u767e\u5ea6\u3001\u5fae\u4fe1\u7b49\uff09\u3002 speedtest : \u5728\u7ebf\u7f51\u7edc\u6d4b\u901f\u7f51\u7ad9\u3002 public-apis : \u516c\u5171 API \u5408\u96c6\u5217\u8868\u3002","title":"\u6742\u9879"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/%E7%BF%BB%E5%A2%99/","text":"\u7ffb\u5899 \u6b64\u94fe\u63a5 \u51fa\u73b0\u5728\u8fd9\u91cc\u7eaf\u5c5e\u4e8c\u8fdb\u5236 bit \u7684\u968f\u610f\u7ec4\u5408\uff0c\u4e0e\u672c\u4eba\u6beb\u65e0\u5173\u7cfb\u3002","title":"\u7ffb\u5899"},{"location":"en/%E5%BF%85%E5%AD%A6%E5%B7%A5%E5%85%B7/%E7%BF%BB%E5%A2%99/#_1","text":"\u6b64\u94fe\u63a5 \u51fa\u73b0\u5728\u8fd9\u91cc\u7eaf\u5c5e\u4e8c\u8fdb\u5236 bit \u7684\u968f\u610f\u7ec4\u5408\uff0c\u4e0e\u672c\u4eba\u6beb\u65e0\u5173\u7cfb\u3002","title":"\u7ffb\u5899"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/CS162/","text":"CS162: Operating System \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A, CS61B, CS61C \u7f16\u7a0b\u8bed\u8a00\uff1aC, x86\u6c47\u7f16 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a200 \u5c0f\u65f6+\uff0c\u4e0a\u4e0d\u5c01\u9876 \u8fd9\u95e8\u8bfe\u8ba9\u6211\u8bb0\u5fc6\u72b9\u65b0\u7684\u6709\u4e24\u4e2a\u90e8\u5206\uff1a \u9996\u5148\u662f\u6559\u6750\uff0c\u8fd9\u672c\u4e66\u7528\u7684\u6559\u6750 Operating Systems: Principles and Practice (2nd Edition) \u4e00\u5171\u56db\u5377\uff0c\u5199\u5f97\u975e\u5e38\u6df1\u5165\u6d45\u51fa\uff0c\u5f88\u597d\u5730\u5f25\u8865\u4e86 MIT6.S081 \u5728\u7406\u8bba\u77e5\u8bc6\u4e0a\u7684\u4e9b\u8bb8\u7a7a\u767d\uff0c\u975e\u5e38\u5efa\u8bae\u5927\u5bb6\u9605\u8bfb\u3002\u76f8\u5173\u8d44\u6e90\u4f1a\u5206\u4eab\u5728\u672c\u4e66\u7684\u7ecf\u5178\u4e66\u7c4d\u63a8\u8350\u6a21\u5757\u3002 \u5176\u6b21\u662f\u8fd9\u95e8\u8bfe\u7684 Project \u2014\u2014 Pintos\u3002Pintos \u662f\u7531 Ben Pfaff \u7b49\u4eba\u5728 x86 \u5e73\u53f0\u4e0a\u7f16\u5199\u7684\u6559\u5b66\u7528\u64cd\u4f5c\u7cfb\u7edf\uff0cBen Pfaff \u751a\u81f3\u4e13\u95e8\u53d1\u4e86\u7bc7 paper \u6765\u9610\u8ff0 Pintos \u7684\u8bbe\u8ba1\u601d\u60f3\u3002 \u548c MIT \u7684 xv6 \u5c0f\u800c\u7cbe\u7684 lab \u8bbe\u8ba1\u7406\u5ff5\u4e0d\u540c\uff0cPintos \u66f4\u6ce8\u91cd\u7cfb\u7edf\u7684 Design and Implementation\u3002Pintos \u672c\u8eab\u4ec5\u4e00\u4e07\u884c\u5de6\u53f3\uff0c\u53ea\u63d0\u4f9b\u4e86\u64cd\u4f5c\u7cfb\u7edf\u6700\u57fa\u672c\u7684\u529f\u80fd\u3002\u800c 4 \u4e2aProject\uff0c\u5c31\u662f\u8ba9\u4f60\u5728\u8fd9\u4e2a\u6781\u4e3a\u7cbe\u7b80\u7684\u64cd\u4f5c\u7cfb\u7edf\u4e4b\u4e0a\uff0c\u5206\u522b\u4e3a\u5176\u589e\u52a0\u7ebf\u7a0b\u8c03\u5ea6\u673a\u5236 (Project1)\uff0c\u7cfb\u7edf\u8c03\u7528 (Project2)\uff0c\u865a\u62df\u5185\u5b58 (Project3) \u4ee5\u53ca\u6587\u4ef6\u7cfb\u7edf (Project4)\u3002\u6240\u6709\u7684 Project \u90fd\u7ed9\u5b66\u751f\u7559\u6709\u5f88\u5927\u7684\u8bbe\u8ba1\u7a7a\u95f4\uff0c\u603b\u4ee3\u7801\u91cf\u5728 2000 \u884c\u5de6\u53f3\u3002\u6839\u636e Stanford \u5b66\u751f \u81ea\u5df1\u7684\u53cd\u9988 \uff0c\u5728 3-4 \u4eba\u7ec4\u961f\u7684\u60c5\u51b5\u4e0b\uff0c\u540e\u4e24\u4e2a Project \u7684\u4eba\u5747\u8017\u65f6\u4e5f\u5728 40 \u4e2a\u5c0f\u65f6\u4ee5\u4e0a\u3002 \u867d\u7136\u96be\u5ea6\u5f88\u5927\uff0c\u4f46 Stanford, Berkeley, JHU \u7b49\u591a\u6240\u7f8e\u56fd\u9876\u5c16\u540d\u6821\u7684\u64cd\u7edf\u8bfe\u7a0b\u5747\u91c7\u7528\u4e86 Pintos\u3002\u5982\u679c\u4f60\u771f\u7684\u5bf9\u64cd\u4f5c\u7cfb\u7edf\u5f88\u611f\u5174\u8da3\uff0cPintos \u4f1a\u6781\u5927\u5730\u63d0\u9ad8\u4f60\u7f16\u5199\u548c debug \u5e95\u5c42\u7cfb\u7edf\u4ee3\u7801\u7684\u80fd\u529b\u3002\u5728\u672c\u79d1\u9636\u6bb5\uff0c\u80fd\u81ea\u5df1\u8bbe\u8ba1\u3001\u5b9e\u73b0\u5e76 debug \u4e00\u4e2a\u5927\u578b\u7cfb\u7edf\uff0c\u662f\u4e00\u6bb5\u975e\u5e38\u73cd\u8d35\u7684\u7ecf\u5386\u3002 \u5317\u5927 2022 \u5e74\u6625\u5b63\u5b66\u671f\u7684\u64cd\u4f5c\u7cfb\u7edf\u5b9e\u9a8c\u73ed\u4e5f\u5c06\u4f1a\u9996\u6b21\u5f15\u5165 Pintos \u4f5c\u4e3a\u8bfe\u7a0b Project\u3002\u6211\u548c\u8be5\u8bfe\u7a0b\u7684 \u53e6\u4e00\u4f4d\u52a9\u6559 \u6574\u7406\u5e76\u5b8c\u5584\u4e86 Pintos \u7684 \u5b9e\u9a8c\u6587\u6863 \uff0c\u5e76\u5229\u7528 Docker 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\u8003\u6838\u4e4b\u4f59\u7684\u7528\u7231\u53d1\u7535\uff0c\u624d\u8ba9\u65e0\u6570\u5b66\u5b50\u6536\u83b7\u4e86\u96be\u5fd8\u7684\u672c\u79d1\u751f\u6daf\u3002\u4e5f\u671f\u5f85\u56fd\u5185\u80fd\u6709\u66f4\u591a\u8fd9\u6837\u7684\u826f\u5fc3\u597d\u8bfe\uff0c\u6211\u4e5f\u4f1a\u7b2c\u4e00\u65f6\u95f4\u6536\u5f55\u8fdb\u672c\u4e66\u4e2d\u8ba9\u66f4\u591a\u4eba\u53d7\u76ca\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/NJUOS/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://jyywiki.cn/OS/2022/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://space.bilibili.com/202224425/channel/collectiondetail?sid=192498 \u8bfe\u7a0b\u6559\u6750\uff1a http://pages.cs.wisc.edu/~remzi/OSTEP/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://jyywiki.cn/OS/2022/","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F/NJUOS/#_3","text":"\u6309\u848b\u8001\u5e08\u7684\u8981\u6c42\uff0c\u6211\u7684\u4f5c\u4e1a\u5b9e\u73b0\u6ca1\u6709\u5f00\u6e90\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITLA/","text":"MIT18.06: Linear Algebra Descriptions Offered by: MIT Prerequisites: English Programming languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: Varying from person to person Gilbert Strang, a great mathematician at MIT, still insists on teaching in his eighties. His classic text book Introduction to Linear Algebra has been adopted as an official textbook by Tsinghua University. After reading the PDF version, I felt deeply guilty and spent more than 200 yuan to purchase a genuine version in English as collection. The cover of this book is attached below. If you can fully understand the mathematical meaning of the cover picture, then your understanding of linear algebra will definitely reach a new height. In addition to the course materials, the famous Youtuber 3Blue1Brown 's video series The Essence of Linear Algebra are also great learning resourses. Resources Course Website: https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/syllabus/ Recordings: refer to course website Textbook: Introduction to Linear Algebra, Gilbert Strang Assignments: refer to course website","title":"MIT18.06: Linear Algebra"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITLA/#mit1806-linear-algebra","text":"","title":"MIT18.06: Linear Algebra"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITLA/#descriptions","text":"Offered by: MIT Prerequisites: English Programming languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: Varying from person to person Gilbert Strang, a great mathematician at MIT, still insists on teaching in his eighties. His classic text book Introduction to Linear Algebra has been adopted as an official textbook by Tsinghua University. After reading the PDF version, I felt deeply guilty and spent more than 200 yuan to purchase a genuine version in English as collection. The cover of this book is attached below. If you can fully understand the mathematical meaning of the cover picture, then your understanding of linear algebra will definitely reach a new height. In addition to the course materials, the famous Youtuber 3Blue1Brown 's video series The Essence of Linear Algebra are also great learning resourses.","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITLA/#resources","text":"Course Website: https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/syllabus/ Recordings: refer to course website Textbook: Introduction to Linear Algebra, Gilbert Strang Assignments: refer to course website","title":"Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/","text":"MIT Calculus Course Descriptions Offered by: MIT Prerequisites: English Programming Languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: Varying from person to person The calculus course at MIT consists of MIT18.01: Single Variable Calculus and MIT18.02: Multivariable Calculus. If you are confident in your math, you can just read the course notes, which are written in a very simple and vivid way, so that you will not be tired of doing homework but can really see the essence of calculus. In addition to the course materials, the famous Youtuber 3Blue1Brown 's video series The Essence of Calculus are also great learning resources. Course Resources Course Website: 18.01 , 18.02 Recordings: refer to course website Textbook: refer to course website Assignments: refer to course website","title":"MIT18.01/18.02: Calculus"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/#mit-calculus-course","text":"","title":"MIT Calculus Course"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/#descriptions","text":"Offered by: MIT Prerequisites: English Programming Languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: Varying from person to person The calculus course at MIT consists of MIT18.01: Single Variable Calculus and MIT18.02: Multivariable Calculus. If you are confident in your math, you can just read the course notes, which are written in a very simple and vivid way, so that you will not be tired of doing homework but can really see the essence of calculus. In addition to the course materials, the famous Youtuber 3Blue1Brown 's video series The Essence of Calculus are also great learning resources.","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/MITmaths/#course-resources","text":"Course Website: 18.01 , 18.02 Recordings: refer to course website Textbook: refer to course website Assignments: refer to course website","title":"Course Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/information/","text":"MIT6.050J: Information theory and Entropy Descriptions Offered by: MIT Prerequisites: None Programming Languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours This is MIT's introductory information theory course for freshmen, Professor Penfield has written a special textbook for this course as course notes, which is in-depth and interesting. Course Resources Course Website: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/index.htm Textbook: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/syllabus/MIT6_050JS08_textbook.pdf Assignments: see the course website for details, including written assignments and Matlab programming assignments.","title":"MIT6.050J: Information theory and Entropy"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/information/#mit6050j-information-theory-and-entropy","text":"","title":"MIT6.050J: Information theory and Entropy"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/information/#descriptions","text":"Offered by: MIT Prerequisites: None Programming Languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours This is MIT's introductory information theory course for freshmen, Professor Penfield has written a special textbook for this course as course notes, which is in-depth and interesting.","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80/information/#course-resources","text":"Course Website: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/index.htm Textbook: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/syllabus/MIT6_050JS08_textbook.pdf Assignments: see the course website for details, including written assignments and Matlab programming assignments.","title":"Course Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/6.042J/","text":"MIT 6.042J: Mathematics for Computer Science Descriptions Offered by: MIT Prerequisites: Calculus, Linear Algebra Programming Languages: Python preferred Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 50-70 hours This is MIT\u2018s discrete mathematics and probability course taught by the notable Tom Leighton (co-founder of Akamai). It is very useful for learning algorithms subsequently. Course Resources Course Website: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/ Recordings: https://www.youtube.com/playlist?list=PLB7540DEDD482705B Assignments: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/assignments/","title":"MIT 6.042J: Mathematics for Computer Science"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/6.042J/#mit-6042j-mathematics-for-computer-science","text":"","title":"MIT 6.042J: Mathematics for Computer Science"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/6.042J/#descriptions","text":"Offered by: MIT Prerequisites: Calculus, Linear Algebra Programming Languages: Python preferred Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 50-70 hours This is MIT\u2018s discrete mathematics and probability course taught by the notable Tom Leighton (co-founder of Akamai). It is very useful for learning algorithms subsequently.","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/6.042J/#course-resources","text":"Course Website: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/ Recordings: https://www.youtube.com/playlist?list=PLB7540DEDD482705B Assignments: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/assignments/","title":"Course Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS126/","text":"UCB CS126 : Probability theory Descriptions Offered by: UC Berkeley Prerequisites: CS70, Calculus, Linear Algebra Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours This is Berkeley's advanced probability course, which involves relatively advanced theoretical content such as statistics and stochastic processes, so a solid mathematical foundation is required. But as long as you stick with it you will certainly take your mastery of probability theory to a new level. The course is designed by Professor Jean Walrand, who has written an accompanying textbook, Probability in Electrical Engineering and Computer Science , in which each chapter uses a specific algorithm as a practical example to demonstrate the application of theory in practice. Such as PageRank, Route Planing, Speech Recognition, etc. The book is open source and can be downloaded as a free PDF or Epub version. Jean Walrand has also created accompanying Python implementations of the examples throughout the book, which are published online as Jupyter Notebook that readers can modify, debug and run them online interactively. In addition to the Homework, nine Labs will allow you to use probability theory to solve practical problems in Python. Course Resources Course Website: https://inst.eecs.berkeley.edu/~ee126/fa20/content.html Textbook: PDF , Epub , Jupyter Notebook Assignments: refer to the course website. Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPic/EECS126 - GitHub","title":"UCB CS126: probability theory"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS126/#ucb-cs126-probability-theory","text":"","title":"UCB CS126 : Probability theory"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS126/#descriptions","text":"Offered by: UC Berkeley Prerequisites: CS70, Calculus, Linear Algebra Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours This is Berkeley's advanced probability course, which involves relatively advanced theoretical content such as statistics and stochastic processes, so a solid mathematical foundation is required. But as long as you stick with it you will certainly take your mastery of probability theory to a new level. The course is designed by Professor Jean Walrand, who has written an accompanying textbook, Probability in Electrical Engineering and Computer Science , in which each chapter uses a specific algorithm as a practical example to demonstrate the application of theory in practice. Such as PageRank, Route Planing, Speech Recognition, etc. The book is open source and can be downloaded as a free PDF or Epub version. Jean Walrand has also created accompanying Python implementations of the examples throughout the book, which are published online as Jupyter Notebook that readers can modify, debug and run them online interactively. In addition to the Homework, nine Labs will allow you to use probability theory to solve practical problems in Python.","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS126/#course-resources","text":"Course Website: https://inst.eecs.berkeley.edu/~ee126/fa20/content.html Textbook: PDF , Epub , Jupyter Notebook Assignments: refer to the course website.","title":"Course Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS126/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPic/EECS126 - GitHub","title":"Personal Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS70/","text":"UCB CS70: Discrete Math and Probability Theory Descriptions Offered by: UC Berkeley Prerequisites: None Programming Languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 60 hours This is Berkeley's introductory discrete mathematics course. The biggest highlight of this course is that it not only teaches you theoretical knowledge, but also introduce the applications of theoretical knowledge in practical algorithms in each module. In this way, students majoring in CS can understand the essence of theoretical knowledge and use it in practice rather than struggle with cold formal mathematical symbols. Specific theory-algorithm correspondences are listed below. Logic proof: stable matching algorithm Graph theory: network topology design Basic number theory: RSA algorithm Polynomial ring: error-correcting code design Probability theory: Hash table design, load balancing, etc. The course notes are also written in a very in-depth manner, with derivations of formulas and practical examples, providing a good reading experience. Course Resources Course Website: http://www.eecs70.org/ Textbook: refer to course website Assignments: refer to course website Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/UCB-CS70 - GitHub","title":"UCB CS70: discrete Math and probability theory"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS70/#ucb-cs70-discrete-math-and-probability-theory","text":"","title":"UCB CS70: Discrete Math and Probability Theory"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS70/#descriptions","text":"Offered by: UC Berkeley Prerequisites: None Programming Languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 60 hours This is Berkeley's introductory discrete mathematics course. The biggest highlight of this course is that it not only teaches you theoretical knowledge, but also introduce the applications of theoretical knowledge in practical algorithms in each module. In this way, students majoring in CS can understand the essence of theoretical knowledge and use it in practice rather than struggle with cold formal mathematical symbols. Specific theory-algorithm correspondences are listed below. Logic proof: stable matching algorithm Graph theory: network topology design Basic number theory: RSA algorithm Polynomial ring: error-correcting code design Probability theory: Hash table design, load balancing, etc. The course notes are also written in a very in-depth manner, with derivations of formulas and practical examples, providing a good reading experience.","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS70/#course-resources","text":"Course Website: http://www.eecs70.org/ Textbook: refer to course website Assignments: refer to course website","title":"Course Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/CS70/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/UCB-CS70 - GitHub","title":"Personal Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/","text":"The Information Theory, Patter Recognition, and Neural Networks Descriptions Offered by: Cambridge Prerequisites: Calculus, Linear Algebra, Probabilities and Statistics Programming Languages: Anything would be OK, Python preferred Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 30-50 hours This is a course on information theory taught by Sir David MacKay at the University of Cambridge. The professor is a very famous scholar in information theory and neural networks, and the textbook for the course is a classic work in the field of information theory. Unfortunately, those whom God loves die young ... Course Resources Course Website: http://www.inference.org.uk/mackay/itila/ Recordings: https://www.youtube.com/playlist?list=PLruBu5BI5n4aFpG32iMbdWoRVAA-Vcso6 Textbooks: Information Theory, Inference, and Learning Algorithms Assignments: At the end of each lesson video, there are post-lesson exercises from the textbook R.I.P Prof. David MacKay","title":"The Information Theory, Patter Recognition, and Neural Networks"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/#the-information-theory-patter-recognition-and-neural-networks","text":"","title":"The Information Theory, Patter Recognition, and Neural Networks"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/#descriptions","text":"Offered by: Cambridge Prerequisites: Calculus, Linear Algebra, Probabilities and Statistics Programming Languages: Anything would be OK, Python preferred Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 30-50 hours This is a course on information theory taught by Sir David MacKay at the University of Cambridge. The professor is a very famous scholar in information theory and neural networks, and the textbook for the course is a classic work in the field of information theory. Unfortunately, those whom God loves die young ...","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/#course-resources","text":"Course Website: http://www.inference.org.uk/mackay/itila/ Recordings: https://www.youtube.com/playlist?list=PLruBu5BI5n4aFpG32iMbdWoRVAA-Vcso6 Textbooks: Information Theory, Inference, and Learning Algorithms Assignments: At the end of each lesson video, there are post-lesson exercises from the textbook","title":"Course Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/The_Information_Theory_Pattern_Recognition_and_Neural_Networks/#rip-prof-david-mackay","text":"","title":"R.I.P Prof. David MacKay"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/","text":"Stanford EE364A: Convex Optimization Descriptions Offered by: Stanford Prerequisites: Python, Calculus, Linear Algebra, Probability Theory, Numerical Analysis Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 150 hours Professor Stephen Boyd is a great expert in the field of convex optimization and his textbook Convex Optimization has been adopted by many prestigious universities. His team has also developed a programming framework for solving common convex optimization problems in Python, Julia, and other popular programming languages, and its homework assignments also use this programming framework to solve real-life convex optimization problems. In practice, you will deeply understand that for the same problem, a small change in the modeling process can make a world of difference in the difficulty of solving the equation. It is an art to make the equations you formulate \"convex\". Course Resources Course Website: http://stanford.edu/class/ee364a/index.html Recordings: https://www.youtube.com/watch?v=VNON98dKjno&list=PLoCMsyE1cvdXeoqd1hGaMBsCAQQ6otUtO Textbook: Convex Optimization Assignments: refer to the course website Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPic/Standford_CVX101 - GitHub","title":"Standford EE364A: Convex Optimization"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/#stanford-ee364a-convex-optimization","text":"","title":"Stanford EE364A: Convex Optimization"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/#descriptions","text":"Offered by: Stanford Prerequisites: Python, Calculus, Linear Algebra, Probability Theory, Numerical Analysis Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 150 hours Professor Stephen Boyd is a great expert in the field of convex optimization and his textbook Convex Optimization has been adopted by many prestigious universities. His team has also developed a programming framework for solving common convex optimization problems in Python, Julia, and other popular programming languages, and its homework assignments also use this programming framework to solve real-life convex optimization problems. In practice, you will deeply understand that for the same problem, a small change in the modeling process can make a world of difference in the difficulty of solving the equation. It is an art to make the equations you formulate \"convex\".","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/#course-resources","text":"Course Website: http://stanford.edu/class/ee364a/index.html Recordings: https://www.youtube.com/watch?v=VNON98dKjno&list=PLoCMsyE1cvdXeoqd1hGaMBsCAQQ6otUtO Textbook: Convex Optimization Assignments: refer to the course website","title":"Course Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/convex/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPic/Standford_CVX101 - GitHub","title":"Personal Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/numerical/","text":"MIT18.330 : Introduction to numerical analysis Descriptions Offered by: MIT Prerequisites:Calculus, Linear Algebra, Probability theory Programming Languages: Julia Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 150 hours While the computational power of computers has been helping people to push boundaries of science, there is a natural barrier between the discrete nature of computers and this continuous world, and how to use discrete representations to estimate and approximate those mathematically continuous concepts is an important theme in numerical analysis. This course will explore various numerical analysis methods in the areas of floating-point representation, equation solving, linear algebra, calculus, and differential equations, allowing you to understand (1) how to design estimation (2) how to estimate errors (3) how to implement algorithms in Julia. There are also plenty of programming assignments to practice these ideas. The designers of this course have also written an open source textbook for this course (see the link below) with plenty of Julia examples. Course Resources Course Website: https://github.com/mitmath/18330 Textbook: https://fncbook.github.io/fnc/frontmatter.html Assignments: 10 problem sets Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPic/MIT18.330 - GitHub","title":"MIT18.330: Introduction to numerical analysis"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/numerical/#mit18330-introduction-to-numerical-analysis","text":"","title":"MIT18.330 : Introduction to numerical analysis"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/numerical/#descriptions","text":"Offered by: MIT Prerequisites:Calculus, Linear Algebra, Probability theory Programming Languages: Julia Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 150 hours While the computational power of computers has been helping people to push boundaries of science, there is a natural barrier between the discrete nature of computers and this continuous world, and how to use discrete representations to estimate and approximate those mathematically continuous concepts is an important theme in numerical analysis. This course will explore various numerical analysis methods in the areas of floating-point representation, equation solving, linear algebra, calculus, and differential equations, allowing you to understand (1) how to design estimation (2) how to estimate errors (3) how to implement algorithms in Julia. There are also plenty of programming assignments to practice these ideas. The designers of this course have also written an open source textbook for this course (see the link below) with plenty of Julia examples.","title":"Descriptions"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/numerical/#course-resources","text":"Course Website: https://github.com/mitmath/18330 Textbook: https://fncbook.github.io/fnc/frontmatter.html Assignments: 10 problem sets","title":"Course Resources"},{"location":"en/%E6%95%B0%E5%AD%A6%E8%BF%9B%E9%98%B6/numerical/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPic/MIT18.330 - GitHub","title":"Personal Resources"},{"location":"en/%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/15445/","text":"CMU 15-445: Database Systems \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aCMU \u5148\u4fee\u8981\u6c42\uff1aC++\uff0c\u6570\u636e\u7ed3\u6784\u4e0e\u7b97\u6cd5 \u7f16\u7a0b\u8bed\u8a00\uff1aC++ 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Solution\u3002\u7279\u522b\u7684\uff0c\u5bf9\u4e8e Homework1\uff0c@ysj1173886760 \u8fd8\u5199\u4e86\u4e00\u4e2a Shell \u811a\u672c\u6765\u5e2e\u5927\u5bb6\u6267\u884c\u81ea\u52a8\u5224\u5206\u3002 \u53e6\u5916\u5728\u8bfe\u7a0b\u7ed3\u675f\u540e\uff0c\u63a8\u8350\u9605\u8bfb\u4e00\u7bc7\u8bba\u6587 Architecture Of a Database System \uff0c\u5bf9\u5e94\u7684\u4e2d\u6587\u7248\u4e5f\u5728\u4e0a\u8ff0\u4ed3\u5e93\u4e2d\u3002\u8bba\u6587\u91cc\u7efc\u8ff0\u4e86\u6570\u636e\u5e93\u7cfb\u7edf\u7684\u6574\u4f53\u67b6\u6784\uff0c\u8ba9\u5927\u5bb6\u53ef\u4ee5\u5bf9\u6570\u636e\u5e93\u6709\u4e00\u4e2a\u66f4\u52a0\u5168\u9762\u7684\u89c6\u91ce\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/15445/#_4","text":"CMU15-721 \u4e3b\u8981\u8bb2\u4e3b\u5b58\u6570\u636e\u5e93\u6709\u5173\u7684\u5185\u5bb9\uff0c\u6bcf\u8282\u8bfe\u90fd\u6709\u5bf9\u5e94\u7684 paper 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\u63d0\u5347 insert \u8bed\u53e5\u7684\u6027\u80fd\uff0c \u540c\u65f6\u4e0d\u4f7f\u6570\u636e\u5e93\u6587\u4ef6\u5927\u5c0f\u8fc7\u5206\u81a8\u80c0\u3002 Assignment2 \u5b9e\u73b0\u4e00\u4e2a\u7b80\u5355\u7684\u8ba1\u5212\u751f\u6210\u5668\uff0c\u5c06\u5404\u79cd\u5df2\u7ecf Parser \u8fc7\u7684 SQL \u8bed\u53e5\u8f6c\u5316\u4e3a\u53ef\u6267\u884c\u7684\u6267\u884c\u8ba1\u5212\u3002 \u4f7f\u7528 nested-loop join \u7b97\u6cd5\uff0c\u5b9e\u73b0\u652f\u6301 inner- and outer-join \u7684 Join \u8ba1\u5212\u8282\u70b9\u3002 \u6dfb\u52a0\u4e00\u4e9b\u5355\u5143\u6d4b\u8bd5\uff0c \u4fdd\u8bc1 inner- and outer-join \u529f\u80fd\u5b9e\u73b0\u6b63\u786e\u3002 Assignment3 \u5b8c\u6210\u6536\u96c6\u8868\u7684\u7edf\u8ba1\u4fe1\u606f\u3002 \u5b8c\u6210\u5404\u79cd\u8ba1\u5212\u8282\u70b9\u7684\u8ba1\u5212\u6210\u672c\u8ba1\u7b97\u3002 \u8ba1\u7b97\u53ef\u51fa\u73b0\u5728\u6267\u884c\u8ba1\u5212\u4e2d\u7684\u5404\u79cd\u8c13\u8bcd\u7684\u9009\u62e9\u6027\u3002 \u6839\u636e\u8c13\u8bcd\u66f4\u65b0\u8ba1\u5212\u8282\u70b9\u8f93\u51fa\u7684\u5143\u7ec4\u7edf\u8ba1\u4fe1\u606f\u3002 \u5269\u4f59 Assignment \u548c Challenges \u53ef\u4ee5\u67e5\u770b\u8bfe\u7a0b\u4ecb\u7ecd\uff0c\u63a8\u8350\u4f7f\u7528 IDEA \u6253\u5f00\u5de5\u7a0b\uff0cMaven \u6784\u5efa\uff0c\u6ce8\u610f\u65e5\u5fd7\u76f8\u5173\u914d\u7f6e\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://courses.cms.caltech.edu/cs122/ \u8bfe\u7a0b\u4ee3\u7801\uff1a https://gitlab.caltech.edu/cs122-19wi \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a7 Assignments + 2 Challenges","title":"Caltech CS122: Database System Implementation"},{"location":"en/%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS122/#caltech-cs-122-database-system-implementation","text":"","title":"Caltech CS 122: Database System Implementation"},{"location":"en/%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS122/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aCaltech \u5148\u4fee\u8981\u6c42\uff1a\u65e0 \u7f16\u7a0b\u8bed\u8a00\uff1aJava \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 \u52a0\u5dde\u7406\u5de5\u7684\u8fd9\u95e8\u8bfe\uff0c\u4e0d\u540c\u4e8e\u6ca1\u6709\u63d0\u4f9b SQL \u5c42\u529f\u80fd\u7684 CMU15-445 \u8bfe\u7a0b\u3002CS122 \u8bfe\u7a0b Lab \u7684\u4fa7\u91cd\u70b9\u5728\u4e8e SQL \u5c42\u7684\u76f8\u5173\u5b9e\u73b0\uff0c\u6d89\u53ca\u67e5\u8be2\u4f18\u5316\u5668\u7684\u5404\u4e2a\u6a21\u5757\uff0c\u6bd4\u5982SQL\u7684\u89e3\u6790\uff0cTranslate\uff0c\u5982\u4f55\u5b9e\u73b0 Join\uff0c\u7edf\u8ba1\u4fe1\u606f\u4ee5\u53ca\u4ee3\u4ef7\u4f30\u8ba1\uff0c\u5b50\u67e5\u8be2\u5b9e\u73b0\uff0cAgg\uff0cGroup By \u7684\u5b9e\u73b0\u7b49\u3002\u9664\u6b64\u4e4b\u5916\uff0c\u8fd8\u6709 B+\u6811\uff0cWAL \u76f8\u5173\u5b9e\u9a8c\u3002\u672c\u95e8\u8bfe\u7a0b\u9002\u5408\u5728\u5b66\u5b8c CMU15-445 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\u8bed\u53e5\u8f6c\u5316\u4e3a\u53ef\u6267\u884c\u7684\u6267\u884c\u8ba1\u5212\u3002 \u4f7f\u7528 nested-loop join \u7b97\u6cd5\uff0c\u5b9e\u73b0\u652f\u6301 inner- and outer-join \u7684 Join \u8ba1\u5212\u8282\u70b9\u3002 \u6dfb\u52a0\u4e00\u4e9b\u5355\u5143\u6d4b\u8bd5\uff0c \u4fdd\u8bc1 inner- and outer-join \u529f\u80fd\u5b9e\u73b0\u6b63\u786e\u3002","title":"Assignment2"},{"location":"en/%E6%95%B0%E6%8D%AE%E5%BA%93%E7%B3%BB%E7%BB%9F/CS122/#assignment3","text":"\u5b8c\u6210\u6536\u96c6\u8868\u7684\u7edf\u8ba1\u4fe1\u606f\u3002 \u5b8c\u6210\u5404\u79cd\u8ba1\u5212\u8282\u70b9\u7684\u8ba1\u5212\u6210\u672c\u8ba1\u7b97\u3002 \u8ba1\u7b97\u53ef\u51fa\u73b0\u5728\u6267\u884c\u8ba1\u5212\u4e2d\u7684\u5404\u79cd\u8c13\u8bcd\u7684\u9009\u62e9\u6027\u3002 \u6839\u636e\u8c13\u8bcd\u66f4\u65b0\u8ba1\u5212\u8282\u70b9\u8f93\u51fa\u7684\u5143\u7ec4\u7edf\u8ba1\u4fe1\u606f\u3002 \u5269\u4f59 Assignment \u548c Challenges 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After understanding the algorithms, you can read the professor's code implementation of all the data structures and algorithms taught in the course. Note that these codes are not demos, but production-ready, time-efficient implementations. They have extensive annotations and comments, and the modularization is also quite good. I learned a lot by just reading the codes. Finally, the most exciting part of the course is the 10 high-quality projects, all with real-world backgrounds, rich test cases, and an automated scoring system (code style is also a part of the scoring). You'll get a taste of algorithms in real life.","title":"Descriptions"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/#course-resources","text":"Course Website: Algorithm I , Algorithm II Recordings: Coursera: Algorithm I , Coursera: lgorithm II , CUvids: Algorithms, 4th Edition Textbooks: Algorithms, 4th Edition Assignments: 10 Projects, the course website has specific requirements","title":"Course Resources"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/Algo/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/Princeton-Algorithm - GitHub .","title":"Personal Resources"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS170/","text":"CS170: Efficient Algorithms and Intractable Problems \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61B, CS70 \u7f16\u7a0b\u8bed\u8a00\uff1aLaTeX \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a60 \u5c0f\u65f6 \u4f2f\u514b\u5229\u7684\u7b97\u6cd5\u8bbe\u8ba1\u8bfe\uff0c\u66f4\u6ce8\u91cd\u7b97\u6cd5\u7684\u7406\u8bba\u57fa\u7840\u4e0e\u590d\u6742\u5ea6\u5206\u6790\u3002\u8bfe\u7a0b\u5185\u5bb9\u6db5\u76d6\u4e86\u5206\u6cbb\u3001\u56fe\u7b97\u6cd5\u3001\u6700\u77ed\u8def\u3001\u751f\u6210\u6811\u3001\u8d2a\u5fc3\u3001\u52a8\u89c4\u3001\u5e76\u67e5\u96c6\u3001\u7ebf\u6027\u89c4\u5212\u3001\u7f51\u7edc\u6d41\u3001NP \u95ee\u9898\u3001\u968f\u673a\u7b97\u6cd5\u3001\u54c8\u5e0c\u7b97\u6cd5\u7b49\u7b49\u3002 \u8fd9\u95e8\u8bfe\u7684\u6559\u6750\u5199\u7684\u5f88\u597d\uff0c\u8bc1\u660e\u6d45\u663e\u6613\u61c2\uff0c\u975e\u5e38\u9002\u5408\u4f5c\u4e3a\u5de5\u5177\u4e66\u67e5\u9605\u3002\u53e6\u5916\uff0c\u8fd9\u95e8\u8bfe\u53ea\u6709\u4e66\u9762\u4f5c\u4e1a\uff0c\u5e76\u4e14\u63a8\u8350\u7528 LaTeX \u7f16\u5199\uff0c\u5927\u5bb6\u53ef\u4ee5\u501f\u6b64\u673a\u4f1a\u953b\u70bc\u81ea\u5df1\u7684 LaTeX \u6280\u5de7\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://cs170.org/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1BU4y1b7RK \u8bfe\u7a0b\u6559\u6750\uff1a\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9 notes \u8bfe\u7a0b\u4f5c\u4e1a\uff1a13 \u6b21\u4e66\u9762\u4f5c\u4e1a\uff0c\u7528 LaTeX \u7f16\u5199 \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/UCB-CS170 - 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GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS61B/","text":"CS61B: Data Structures and Algorithms Descriptions Offered by: UC Berkeley Prerequisites: CS61A Programming Languages: Java Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 60 hours It is the second course of UC Berkeley's CS61 series. It mainly focuses on the design of data structures and algorithms as well as giving students the opportunity to be exposed to thousands of lines of engineering code and gain a preliminary understanding of software engineering through Java. I took the version for 2018 Spring. Josh Hug, the instructor, generously made the autograder open-source. You can use gradescope invitation code published on the website for free and easily test your implementation. All programming assignments in this course are done in Java. Students without Java experience don't have to worry. There will be detailed tutorials in the course from the configuration of IDEA to the core syntax and features of Java. The quality of homework in this class is also unparalleled. The 14 labs will allow you to implement most of the data structures mentioned in the class by yourself, and the 10 homework will allow you to use data structures and algorithms to solve practical problems. In addition, there are 3 projects that give you the opportunity to be exposed to thousands of lines of engineering code and enhance your Java skills in practice. Resources Course Resources Course Website: https://sp18.datastructur.es/ Recordings: refer to the course website Textbook: None Assignments: Slightly different every year. In the spring semester of 2018, there are 14 Labs, 10 Homeworks and 3 Projects. Please refer to the course website for specific requirements. Personal resources All resources and homework implementations used by @PKUFlyingPig in this course are summarized in PKUFlyingPig/CS61B - GitHub .","title":"UCB CS61B: Data Structures and Algorithms"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS61B/#cs61b-data-structures-and-algorithms","text":"","title":"CS61B: Data Structures and Algorithms"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS61B/#descriptions","text":"Offered by: UC Berkeley Prerequisites: CS61A Programming Languages: Java Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 60 hours It is the second course of UC Berkeley's CS61 series. It mainly focuses on the design of data structures and algorithms as well as giving students the opportunity to be exposed to thousands of lines of engineering code and gain a preliminary understanding of software engineering through Java. I took the version for 2018 Spring. Josh Hug, the instructor, generously made the autograder open-source. You can use gradescope invitation code published on the website for free and easily test your implementation. All programming assignments in this course are done in Java. Students without Java experience don't have to worry. There will be detailed tutorials in the course from the configuration of IDEA to the core syntax and features of Java. The quality of homework in this class is also unparalleled. The 14 labs will allow you to implement most of the data structures mentioned in the class by yourself, and the 10 homework will allow you to use data structures and algorithms to solve practical problems. In addition, there are 3 projects that give you the opportunity to be exposed to thousands of lines of engineering code and enhance your Java skills in practice.","title":"Descriptions"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS61B/#resources","text":"","title":"Resources"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS61B/#course-resources","text":"Course Website: https://sp18.datastructur.es/ Recordings: refer to the course website Textbook: None Assignments: Slightly different every year. In the spring semester of 2018, there are 14 Labs, 10 Homeworks and 3 Projects. Please refer to the course website for specific requirements.","title":"Course Resources"},{"location":"en/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95/CS61B/#personal-resources","text":"All resources and homework implementations used by @PKUFlyingPig in this course are summarized in PKUFlyingPig/CS61B - GitHub .","title":"Personal resources"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/CS189/","text":"CS189: Introduction to Machine Learning \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS188, CS70 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u6211\u6ca1\u6709\u7cfb\u7edf\u4e0a\u8fc7\uff0c\u53ea\u662f\u628a\u5b83\u7684\u8bfe\u7a0b notes \u4f5c\u4e3a\u5de5\u5177\u4e66\u67e5\u9605\u3002\u4e0d\u8fc7\u4ece\u8bfe\u7a0b\u7f51\u7ad9\u4e0a\u6765\u770b\uff0c\u5b83\u6bd4 CS229 \u597d\u7684\u662f\u5f00\u6e90\u4e86\u6240\u6709 homework \u7684\u4ee3\u7801\u4ee5\u53ca gradescope \u7684 autograder\u3002\u540c\u6837\uff0c\u8fd9\u95e8\u8bfe\u8bb2\u5f97\u76f8\u5f53\u7406\u8bba\u4e14\u6df1\u5165\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.eecs189.org/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/playlist?list=PLOOm2AoWIPEyZazQVnIcaK2KnezpGZV-X \u8bfe\u7a0b\u6559\u6750\uff1a https://www.eecs189.org/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://www.eecs189.org/","title":"UCB CS189: Introduction to Machine Learning"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/CS189/#cs189-introduction-to-machine-learning","text":"","title":"CS189: Introduction to Machine Learning"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/CS189/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS188, CS70 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u6211\u6ca1\u6709\u7cfb\u7edf\u4e0a\u8fc7\uff0c\u53ea\u662f\u628a\u5b83\u7684\u8bfe\u7a0b notes \u4f5c\u4e3a\u5de5\u5177\u4e66\u67e5\u9605\u3002\u4e0d\u8fc7\u4ece\u8bfe\u7a0b\u7f51\u7ad9\u4e0a\u6765\u770b\uff0c\u5b83\u6bd4 CS229 \u597d\u7684\u662f\u5f00\u6e90\u4e86\u6240\u6709 homework \u7684\u4ee3\u7801\u4ee5\u53ca gradescope \u7684 autograder\u3002\u540c\u6837\uff0c\u8fd9\u95e8\u8bfe\u8bb2\u5f97\u76f8\u5f53\u7406\u8bba\u4e14\u6df1\u5165\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/CS189/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.eecs189.org/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/playlist?list=PLOOm2AoWIPEyZazQVnIcaK2KnezpGZV-X \u8bfe\u7a0b\u6559\u6750\uff1a https://www.eecs189.org/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://www.eecs189.org/","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/CS229/","text":"CS229: Machine Learning \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u9ad8\u6570\uff0c\u6982\u7387\u8bba\uff0cPython\uff0c\u9700\u8981\u8f83\u6df1\u539a\u7684\u6570\u5b66\u529f\u5e95 \u7f16\u7a0b\u8bed\u8a00\uff1a\u65e0 \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u540c\u6837\u662f\u5434\u6069\u8fbe\u8bb2\u6388\uff0c\u4f46\u662f\u8fd9\u662f\u4e00\u95e8\u7814\u7a76\u751f\u8bfe\u7a0b\uff0c\u6240\u4ee5\u66f4\u504f\u91cd\u6570\u5b66\u7406\u8bba\uff0c\u4e0d\u6ee1\u8db3\u4e8e\u8c03\u5305\u800c\u60f3\u6df1\u5165\u7406\u89e3\u7b97\u6cd5\u672c\u8d28\uff0c\u6216\u8005\u6709\u5fd7\u4e8e\u4ece\u4e8b\u673a\u5668\u5b66\u4e60\u7406\u8bba\u7814\u7a76\u7684\u540c\u5b66\u53ef\u4ee5\u5b66\u4e60\u8fd9\u95e8\u8bfe\u7a0b\u3002\u8bfe\u7a0b\u7f51\u7ad9\u4e0a\u63d0\u4f9b\u4e86\u6240\u6709\u7684\u8bfe\u7a0b notes\uff0c\u5199\u5f97\u975e\u5e38\u4e13\u4e1a\u4e14\u7406\u8bba\uff0c\u9700\u8981\u4e00\u5b9a\u7684\u6570\u5b66\u529f\u5e95\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://cs229.stanford.edu/syllabus.html \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1JE411w7Ub \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0\uff0c\u8bfe\u7a0b notes \u5199\u5f97\u975e\u5e38\u597d \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u4e0d\u5bf9\u516c\u4f17\u5f00\u653e \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS229 - 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He is one of the founders of the famous online education platform Coursera , and also a famous professor at Stanford. This introductory machine learning course must be one of his famous works (the other is his deep learning course), and has hundreds of thousands of learners on Coursera (note that these are people who paid for the certificate, which costs several hundred dollars), and the number of nonpaying learners should be far more than that. The class is extremely friendly to novices, and Andrew has the ability to make machine learning as straightforward as 1+1=2. You'll learn about linear regression, logistic regression, support vector machines, unsupervised learning, dimensionality reduction, anomaly detection, and recommender systems, etc. and solidify your understanding with hands-on programming. The quality of the assignments needs no word to say. With detailed code frameworks and practical background, you can use what you've learned to solve real problems. 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Also, course material is available on Coursera.","title":"Coursera: Machine Learning"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/ML/#coursera-machine-learning","text":"","title":"Coursera: Machine Learning"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/ML/#descriptions","text":"Offered by: Stanford Prerequisites: entry level of AI and proficient in Python Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours When it comes to Andrew Ng, no one in the AI community should be unaware of him. He is one of the founders of the famous online education platform Coursera , and also a famous professor at Stanford. 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Also, course material is available on Coursera.","title":"Personal Resources"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%B3%BB%E7%BB%9F/CMU10-414/","text":"CMU 10-414/714: Deep Learning Systems \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aCMU \u5148\u4fee\u8981\u6c42\uff1a\u7cfb\u7edf\u5165\u95e8(eg.15-213)\u3001\u6df1\u5ea6\u5b66\u4e60\u5165\u95e8\u3001\u57fa\u672c\u7684\u6570\u5b66\u77e5\u8bc6 \u7f16\u7a0b\u8bed\u8a00\uff1aN/A\uff08\u636e\u8bfe\u7a0b\u4e3b\u9875\uff0c\u8981\u6c42\u719f\u6089Python\u3001C/C++\uff09 \u8bfe\u7a0b\u96be\u5ea6\uff1aN/A \u9884\u8ba1\u5b66\u65f6\uff1aN/A \u8fd9\u662f CMU 2022\u5e74\u79cb\u5b63\u5b66\u671f\u5f00\u8bbe\u7684\u4e00\u95e8\u65b0\u8bfe\uff0c\u805a\u7126\u4e8e\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u7684\u5177\u4f53\u5b9e\u73b0\uff0c\u8bfe\u7a0b Project \u4f1a\u5b9e\u73b0\u4e00\u4e2a\u8ff7\u4f60\u7684\u7c7b\u4f3c\u4e8e Pytorch 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\u8fd9\u95e8\u8bfe\u662f\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u9886\u57df\u7684\u9876\u5c16\u5b66\u8005\u9648\u5929\u5947\u57282022\u5e74\u6691\u671f\u5f00\u8bbe\u7684\u4e00\u95e8\u5728\u7ebf\u8bfe\u7a0b\u3002\u5176\u5b9e\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u65e0\u8bba\u5728\u5de5\u4e1a\u754c\u8fd8\u662f\u5b66\u672f\u754c\u4ecd\u7136\u662f\u4e00\u4e2a\u975e\u5e38\u524d\u6cbf\u4e14\u5feb\u901f\u66f4\u8fed\u7684\u9886\u57df\uff0c\u56fd\u5185\u5916\u6b64\u524d\u8fd8\u6ca1\u6709\u4e3a\u8fd9\u4e2a\u65b9\u5411\u4e13\u95e8\u5f00\u8bbe\u7684\u76f8\u5173\u8bfe\u7a0b\u3002\u56e0\u6b64\u5982\u679c\u5bf9\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u611f\u5174\u8da3\u60f3\u6709\u4e2a\u5168\u8c8c\u6027\u7684\u611f\u77e5\u7684\u8bdd\uff0c\u53ef\u4ee5\u5b66\u4e60\u4e00\u4e0b\u8fd9\u95e8\u8bfe\u3002 \u672c\u8bfe\u7a0b\u4e3b\u8981\u4ee5 Apache TVM 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Compilation"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%B3%BB%E7%BB%9F/MLC/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aBilibili \u5927\u5b66 \u5148\u4fee\u8981\u6c42\uff1a\u673a\u5668\u5b66\u4e60/\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a30\u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u662f\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u9886\u57df\u7684\u9876\u5c16\u5b66\u8005\u9648\u5929\u5947\u57282022\u5e74\u6691\u671f\u5f00\u8bbe\u7684\u4e00\u95e8\u5728\u7ebf\u8bfe\u7a0b\u3002\u5176\u5b9e\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u65e0\u8bba\u5728\u5de5\u4e1a\u754c\u8fd8\u662f\u5b66\u672f\u754c\u4ecd\u7136\u662f\u4e00\u4e2a\u975e\u5e38\u524d\u6cbf\u4e14\u5feb\u901f\u66f4\u8fed\u7684\u9886\u57df\uff0c\u56fd\u5185\u5916\u6b64\u524d\u8fd8\u6ca1\u6709\u4e3a\u8fd9\u4e2a\u65b9\u5411\u4e13\u95e8\u5f00\u8bbe\u7684\u76f8\u5173\u8bfe\u7a0b\u3002\u56e0\u6b64\u5982\u679c\u5bf9\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u611f\u5174\u8da3\u60f3\u6709\u4e2a\u5168\u8c8c\u6027\u7684\u611f\u77e5\u7684\u8bdd\uff0c\u53ef\u4ee5\u5b66\u4e60\u4e00\u4e0b\u8fd9\u95e8\u8bfe\u3002 \u672c\u8bfe\u7a0b\u4e3b\u8981\u4ee5 Apache TVM \u8fd9\u4e00\u4e3b\u6d41\u7684\u673a\u5668\u5b66\u4e60\u7f16\u8bd1\u6846\u67b6\u4e3a\u4f8b\uff08\u9648\u5929\u5947\u662f\u8fd9\u4e2a\u6846\u67b6\u7684\u521b\u59cb\u4eba\u4e4b\u4e00\uff09\uff0c\u805a\u7126\u4e8e\u5982\u4f55\u5c06\u5f00\u53d1\u6a21\u5f0f\u4e0b\uff08\u5982 Tensorflow, Pytorch, Jax\uff09\u7684\u5404\u7c7b\u673a\u5668\u5b66\u4e60\u6a21\u578b\uff0c\u901a\u8fc7\u4e00\u5957\u666e\u9002\u7684\u62bd\u8c61\u548c\u4f18\u5316\u7b97\u6cd5\uff0c\u53d8\u6362\u4e3a\u62e5\u6709\u66f4\u9ad8\u6027\u80fd\u5e76\u4e14\u9002\u914d\u5404\u7c7b\u5e95\u5c42\u786c\u4ef6\u7684\u90e8\u7f72\u6a21\u5f0f\u3002\u8bfe\u7a0b\u8bb2\u6388\u7684\u77e5\u8bc6\u70b9\u90fd\u662f\u76f8\u5bf9 High-Level \u7684\u5b8f\u89c2\u6982\u5ff5\uff0c\u540c\u65f6\u6bcf\u8282\u8bfe\u90fd\u4f1a\u6709\u4e00\u4e2a\u914d\u5957\u7684 Jupyter Notebook \u6765\u901a\u8fc7\u5177\u4f53\u7684\u4ee3\u7801\u8bb2\u89e3\u77e5\u8bc6\u70b9\uff0c\u56e0\u6b64\u5982\u679c\u4ece\u4e8b TVM \u76f8\u5173\u7684\u7f16\u7a0b\u5f00\u53d1\u7684\u8bdd\uff0c\u8fd9\u95e8\u8bfe\u6709\u4e30\u5bcc\u4e14\u89c4\u8303\u7684\u4ee3\u7801\u793a\u4f8b\u4ee5\u4f9b\u53c2\u8003\u3002 \u6240\u6709\u7684\u8bfe\u7a0b\u8d44\u6e90\u5168\u90e8\u5f00\u6e90\u5e76\u4e14\u6709\u4e2d\u6587\u548c\u82f1\u6587\u4e24\u4e2a\u7248\u672c\uff0cB\u7ad9\u548c\u6cb9\u7ba1\u5206\u522b\u6709\u4e2d\u6587\u548c\u82f1\u6587\u7684\u8bfe\u7a0b\u5f55\u5f71\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%B3%BB%E7%BB%9F/MLC/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://mlc.ai/summer22-zh/ \u8bfe\u7a0b\u89c6\u9891\uff1a Bilibili \u8bfe\u7a0b\u7b14\u8bb0\uff1a https://mlc.ai/zh/index.html \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://github.com/mlc-ai/notebooks/blob/main/assignment","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CMU10-708/","text":"CMU 10-708: Probabilistic Graphical Models \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aCMU \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://sailinglab.github.io/pgm-spring-2019/ \u8fd9\u4e2a\u7f51\u7ad9\u5305\u542b\u4e86\u6240\u6709\u7684\u8d44\u6e90\uff1aslides, nots, video, homework, project \u8fd9\u95e8\u8bfe\u7a0b\u662f CMU \u7684\u56fe\u6a21\u578b\u57fa\u7840 + \u8fdb\u9636\u8bfe\uff0c\u6388\u8bfe\u8001\u5e08\u4e3a Eric P. Xing\uff0c\u6db5\u76d6\u4e86\u56fe\u6a21\u578b\u57fa\u7840\uff0c\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u5728\u5f3a\u5316\u5b66\u4e60\u4e2d\u7684\u5e94\u7528\uff0c\u4ee5\u53ca\u975e\u53c2\u6570\u65b9\u6cd5\u3002\u76f8\u5f53\u786c\u6838","title":"CMU 10-708: Probabilistic Graphical Models"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CMU10-708/#cmu-10-708-probabilistic-graphical-models","text":"","title":"CMU 10-708: Probabilistic Graphical Models"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CMU10-708/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aCMU \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://sailinglab.github.io/pgm-spring-2019/ \u8fd9\u4e2a\u7f51\u7ad9\u5305\u542b\u4e86\u6240\u6709\u7684\u8d44\u6e90\uff1aslides, nots, video, homework, project \u8fd9\u95e8\u8bfe\u7a0b\u662f CMU \u7684\u56fe\u6a21\u578b\u57fa\u7840 + \u8fdb\u9636\u8bfe\uff0c\u6388\u8bfe\u8001\u5e08\u4e3a Eric P. Xing\uff0c\u6db5\u76d6\u4e86\u56fe\u6a21\u578b\u57fa\u7840\uff0c\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u5728\u5f3a\u5316\u5b66\u4e60\u4e2d\u7684\u5e94\u7528\uff0c\u4ee5\u53ca\u975e\u53c2\u6570\u65b9\u6cd5\u3002\u76f8\u5f53\u786c\u6838","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CS229M/","text":"STATS214 / CS229M: Machine Learning Theory \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Statistics \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/stats214/ \u7ecf\u5178\u5b66\u4e60\u7406\u8bba + \u6700\u65b0\u6df1\u5ea6\u5b66\u4e60\u7406\u8bba\uff0c\u975e\u5e38\u786c\u6838\u3002\u6388\u8bfe\u8001\u5e08\u4e4b\u524d\u662f Percy Liang\uff0c\u73b0\u5728\u662f Tengyu Ma","title":"Stanford STATS214 / CS229M: Machine Learning Theory"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CS229M/#stats214-cs229m-machine-learning-theory","text":"","title":"STATS214 / CS229M: Machine Learning Theory"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/CS229M/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Statistics \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/stats214/ \u7ecf\u5178\u5b66\u4e60\u7406\u8bba + \u6700\u65b0\u6df1\u5ea6\u5b66\u4e60\u7406\u8bba\uff0c\u975e\u5e38\u786c\u6838\u3002\u6388\u8bfe\u8001\u5e08\u4e4b\u524d\u662f Percy Liang\uff0c\u73b0\u5728\u662f Tengyu Ma","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STA4273/","text":"STA 4273 Winter 2021: Minimizing Expectations \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aU Toronto \u5148\u4fee\u8981\u6c42\uff1aBayesian Inference, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.cs.toronto.edu/~cmaddis/courses/sta4273_w21/ \u8fd9\u662f\u4e00\u95e8\u8f83\u4e3a\u8fdb\u9636\u7684 Ph.D. \u7814\u7a76\u8bfe\u7a0b\uff0c\u6838\u5fc3\u5185\u5bb9\u662f inference \u548c control \u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u6388\u8bfe\u8001\u5e08\u4e3a Chris Maddison (AlphaGo founding member, NeurIPS 14 best paper)","title":"U Toronto STA 4273 Winter 2021: Minimizing Expectations"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STA4273/#sta-4273-winter-2021-minimizing-expectations","text":"","title":"STA 4273 Winter 2021: Minimizing Expectations"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STA4273/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aU Toronto \u5148\u4fee\u8981\u6c42\uff1aBayesian Inference, Reinforcement Learning \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.cs.toronto.edu/~cmaddis/courses/sta4273_w21/ \u8fd9\u662f\u4e00\u95e8\u8f83\u4e3a\u8fdb\u9636\u7684 Ph.D. \u7814\u7a76\u8bfe\u7a0b\uff0c\u6838\u5fc3\u5185\u5bb9\u662f inference \u548c control \u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u6388\u8bfe\u8001\u5e08\u4e3a Chris Maddison (AlphaGo founding member, NeurIPS 14 best paper)","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STAT8201/","text":"Columbia STAT 8201: Deep Generative Models \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aColumbia University \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Graphical Models \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://stat.columbia.edu/~cunningham/teaching/GR8201/ \u8fd9\u95e8\u8bfe\u662f\u4e00\u95e8 PhD \u8ba8\u8bba\u73ed\uff0c\u6bcf\u5468\u7684\u5185\u5bb9\u662f\u5c55\u793a + \u8ba8\u8bba\u8bba\u6587\uff0c\u6388\u8bfe\u8001\u5e08\u662f John Cunningham\u3002Deep Generative Models \uff08\u6df1\u5ea6\u751f\u6210\u6a21\u578b\uff09 \u662f\u56fe\u6a21\u578b\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u4e5f\u662f\u73b0\u4ee3\u673a\u5668\u5b66\u4e60\u6700\u91cd\u8981\u7684\u65b9\u5411\u4e4b\u4e00","title":"Columbia STAT 8201: Deep Generative Models"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STAT8201/#columbia-stat-8201-deep-generative-models","text":"","title":"Columbia STAT 8201: Deep Generative Models"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/STAT8201/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aColumbia University \u5148\u4fee\u8981\u6c42\uff1aMachine Learning, Deep Learning, Graphical Models \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://stat.columbia.edu/~cunningham/teaching/GR8201/ \u8fd9\u95e8\u8bfe\u662f\u4e00\u95e8 PhD \u8ba8\u8bba\u73ed\uff0c\u6bcf\u5468\u7684\u5185\u5bb9\u662f\u5c55\u793a + \u8ba8\u8bba\u8bba\u6587\uff0c\u6388\u8bfe\u8001\u5e08\u662f John Cunningham\u3002Deep Generative Models \uff08\u6df1\u5ea6\u751f\u6210\u6a21\u578b\uff09 \u662f\u56fe\u6a21\u578b\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u5408\uff0c\u4e5f\u662f\u73b0\u4ee3\u673a\u5668\u5b66\u4e60\u6700\u91cd\u8981\u7684\u65b9\u5411\u4e4b\u4e00","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/","text":"\u673a\u5668\u5b66\u4e60\u8fdb\u9636 \u6b64\u8def\u7ebf\u56fe\u9002\u7528\u4e8e\u5df2\u7ecf\u5b66\u8fc7\u4e86\u57fa\u7840\u673a\u5668\u5b66\u4e60 (ML, NLP, CV, RL) \u7684\u540c\u5b66 (\u9ad8\u5e74\u7ea7\u672c\u79d1\u751f\u6216\u4f4e\u5e74\u7ea7\u7814\u7a76\u751f)\uff0c\u5df2\u7ecf\u53d1\u8868\u8fc7\u81f3\u5c11\u4e00\u7bc7\u9876\u4f1a\u8bba\u6587 (NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, CVPR, ICCV) \u60f3\u8981\u8d70\u673a\u5668\u5b66\u4e60\u79d1\u7814\u8def\u7ebf\u7684\u9009\u624b\u3002 \u6b64\u8def\u7ebf\u7684\u76ee\u6807\u662f\u4e3a\u8bfb\u61c2\u4e0e\u53d1\u8868\u673a\u5668\u5b66\u4e60\u9876\u4f1a\u8bba\u6587\u6253\u4e0b\u7406\u8bba\u57fa\u7840\uff0c\u7279\u522b\u662f Probabilistic Methods \u8fd9\u4e2a track \u4e0b\u7684\u6587\u7ae0 \u673a\u5668\u5b66\u4e60\u8fdb\u9636\u53ef\u80fd\u5b58\u5728\u591a\u79cd\u4e0d\u540c\u7684\u5b66\u4e60\u8def\u7ebf\uff0c\u6b64\u8def\u7ebf\u53ea\u80fd\u4ee3\u8868\u4f5c\u8005 Yao Fu \u6240\u7406\u89e3\u7684\u6700\u4f73\u8def\u5f84\uff0c\u4fa7\u91cd\u4e8e\u8d1d\u53f6\u65af\u5b66\u6d3e\u4e0b\u7684\u6982\u7387\u5efa\u6a21\u65b9\u6cd5\uff0c\u4e5f\u4f1a\u6d89\u53ca\u5230\u5404\u9879\u76f8\u5173\u5b66\u79d1\u7684\u4ea4\u53c9\u77e5\u8bc6\u3002 \u5fc5\u8bfb\u6559\u6750 PRML: Pattern Recognition and Machine Learning. Christopher Bishop \u7ecf\u5178\u8d1d\u53f6\u65af\u5b66\u6d3e\u6559\u6750 AoS: All of Statistics. Larry Wasserman \u7ecf\u5178\u9891\u7387\u5b66\u6d3e\u6559\u6750 \u6240\u4ee5\u8fd9\u4e24\u672c\u4e66\u521a\u597d\u76f8\u8f85\u76f8\u6210 \u5b57\u5178 MLAPP: Machine Learning: A Probabilistic Perspective. Kevin Murphy Convex Optimization. Stephen Boyd and Lieven Vandenberghe \u8fdb\u9636\u4e66\u7c4d W&J: Graphical Models, Exponential Families, and Variational Inference. Martin Wainwright and Michael Jordan Theory of Point Estimation. E. L. Lehmann and George Casella \u5982\u4f55\u9605\u8bfb Guidelines \u5fc5\u8bfb\u6559\u6750\u5c31\u662f\u4e00\u5b9a\u8981\u8bfb\u7684\u6559\u6750 \u5b57\u5178\u7684\u610f\u601d\u662f\uff0c\u4e00\u822c\u60c5\u51b5\u4e0b\u4e0d\u7ba1\u5b83\uff0c\u4f46\u5f53\u9047\u5230\u4e86\u4e0d\u61c2\u7684\u6982\u5ff5\u7684\u65f6\u5019\uff0c\u5c31\u53bb\u5b57\u5178\u91cc\u9762\u67e5\uff08\u800c\u4e0d\u662f\u7ef4\u57fa\u767e\u79d1\uff09 \u8fdb\u9636\u4e66\u7c4d\u5148\u4e0d\u8bfb\uff0c\u5148\u8bfb\u5b8c\u5fc5\u8bfb\u4e66\u7c4d\u3002\u5fc5\u8bfb\u4e66\u7c4d\u4e00\u822c\u90fd\u662f\u8981\u524d\u524d\u540e\u540e\u53cd\u590d\u770b\u8fc7 N \u904d\u624d\u7b97\u8bfb\u5b8c \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u6700\u91cd\u8981\u7684\u8bfb\u6cd5\u5c31\u662f\u5bf9\u6bd4\u9605\u8bfb (contrastive-comparative reading)\uff1a\u540c\u65f6\u6253\u5f00\u4e24\u672c\u4e66\u8bb2\u540c\u4e00\u4e3b\u9898\u7684\u7ae0\u8282\uff0c\u7136\u540e\u5bf9\u6bd4\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u5c3d\u91cf\u53bb\u56de\u60f3\u4e4b\u524d\u8bfb\u8fc7\u7684\u8bba\u6587\uff0c\u6bd4\u8f83\u8bba\u6587\u548c\u6559\u6750\u7684\u76f8\u540c\u70b9\u4e0e\u4e0d\u540c\u70b9 \u57fa\u7840\u8def\u5f84 \u5148\u8bfb AoS \u7b2c\u516d\u7ae0: Models, Statistical Inference and Learning\uff0c\u8fd9\u4e00\u90e8\u5206\u662f\u6700\u57fa\u7840\u7684\u79d1\u666e \u7136\u540e\u8bfb PRML \u7b2c 10, 11 \u7ae0 \u7b2c 10 \u7ae0\u7684\u5185\u5bb9\u662f Variational Inference, \u7b2c 11 \u7ae0\u7684\u5185\u5bb9\u662f MCMC, \u8fd9\u4e24\u79cd\u65b9\u6cd5\u662f\u8d1d\u53f6\u65af\u63a8\u65ad\u7684\u4e24\u6761\u6700\u4e3b\u8981\u8def\u7ebf \u5982\u679c\u5728\u8bfb PRML \u7684\u8fc7\u7a0b\u4e2d\u53d1\u73b0\u6709\u4efb\u4f55\u4e0d\u61c2\u7684\u540d\u8bcd\uff0c\u5c31\u53bb\u7ffb\u524d\u9762\u7684\u7ae0\u8282\u3002\u5f88\u5927\u6982\u7387\u80fd\u591f\u5728\u7b2c 3\uff0c4 \u7ae0\u627e\u5230\u76f8\u5bf9\u5e94\u7684\u5b9a\u4e49\uff1b\u5982\u679c\u627e\u4e0d\u5230\u6216\u8005\u4e0d\u591f\u8be6\u7ec6\uff0c\u5c31\u53bb\u67e5 MLAPP AoS \u7b2c 8 \u7ae0 (Parametric Inference) \u548c\u7b2c 11 \u7ae0 (Bayesian Inference) \u4e5f\u53ef\u4ee5\u4f5c\u4e3a\u53c2\u8003\u3002\u6700\u597d\u7684\u65b9\u6cd5\u662f\u591a\u672c\u4e66\u5bf9\u6bd4\u9605\u8bfb\uff0c\u6d41\u7a0b\u5982\u4e0b \u5047\u8bbe\u6211\u5728\u8bfb PRML \u7b2c 10 \u7ae0\u7684\u65f6\u5019\u53d1\u73b0\u4e86\u4e00\u4e2a\u4e0d\u61c2\u7684\u8bcd\uff1aposterior inference \u4e8e\u662f\u6211\u5f80\u524d\u7ffb\uff0c\u7ffb\u5230\u4e86\u7b2c 3 \u7ae0 (Linear Model for Regression)\uff0c\u770b\u5230\u4e86\u6700\u7b80\u5355\u7684 posterior \u7136\u540e\u6211\u63a5\u7740\u7ffb AoS\uff0c\u7ffb\u5230\u4e86\u7b2c 11 \u7ae0\uff0c\u4e5f\u6709\u5bf9 posterior \u7684\u63cf\u8ff0 \u7136\u540e\u6211\u5bf9\u6bd4 PRML \u7b2c 10 \u7ae0\uff0c\u7b2c 3 \u7ae0\uff0cAoS \u7b2c 11 \u7ae0\uff0c\u4e09\u5904\u4e0d\u540c\u5730\u65b9\u5bf9 posterior \u7684\u89e3\u8bfb\uff0c\u6bd4\u8f83\u5176\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u5b8c PRML \u7b2c 10 \u548c 11 \u7ae0\u4e4b\u540e\uff0c\u63a5\u7740\u8bfb AoS \u7b2c 24 \u7ae0 (Simulation Methods)\uff0c\u7136\u540e\u628a\u5b83\u548c PRML \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb -- \u8fd9\u4fe9\u90fd\u662f\u8bb2 MCMC \u5982\u679c\u5230\u6b64\u5904\u53d1\u73b0\u8fd8\u6709\u57fa\u7840\u6982\u5ff5\u8bfb\u4e0d\u61c2\uff0c\u5c31\u56de\u5230 PRML \u7b2c 3 \u7ae0\uff0c\u628a\u5b83\u548c AoS \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb Again\uff0c\u5bf9\u6bd4\u9605\u8bfb\u975e\u5e38\u91cd\u8981\uff0c\u4e00\u5b9a\u8981\u628a\u4e0d\u540c\u672c\u4e66\u7684\u7c7b\u4f3c\u5185\u5bb9\u540c\u65f6\u6446\u5728\u9762\u524d\u76f8\u4e92\u5bf9\u6bd4\uff0c\u8fd9\u6837\u53ef\u4ee5\u663e\u8457\u589e\u5f3a\u8bb0\u5fc6 \u7136\u540e\u8bfb PRML \u7b2c 13 \u7ae0\uff08\u8df3\u8fc7\u7b2c 12 \u7ae0\uff09\uff0c\u8fd9\u4e00\u7ae0\u53ef\u4ee5\u548c MLAPP \u7684\u7b2c 17, 18 \u7ae0\u5bf9\u6bd4\u9605\u8bfb MLAPP \u7b2c 17 \u7ae0\u662f PRML \u7b2c 13.2 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 HMM MLAPP \u7b2c 18 \u7ae0\u662f PRML \u7b2c 13.3 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 LDS \u8bfb\u5b8c PRML \u7b2c 13 \u7ae0\u4e4b\u540e\uff0c\u518d\u53bb\u8bfb PRML \u7b2c 8 \u7ae0 (Graphical Models) -- \u6b64\u65f6\u8fd9\u90e8\u5206\u5e94\u8be5\u4f1a\u8bfb\u5f97\u5f88\u8f7b\u677e \u4ee5\u4e0a\u7684\u5185\u5bb9\u53ef\u4ee5\u8fdb\u4e00\u6b65\u5bf9\u7167 CMU 10-708 PGM \u8bfe\u7a0b\u6750\u6599 \u5230\u76ee\u524d\u4e3a\u6b62\uff0c\u5e94\u8be5\u80fd\u591f\u638c\u63e1 \u6982\u7387\u6a21\u578b\u7684\u57fa\u7840\u5b9a\u4e49 \u7cbe\u51c6\u63a8\u65ad - Sum-Product \u8fd1\u4f3c\u63a8\u65ad - MCMC \u8fd1\u4f3c\u63a8\u65ad - VI \u7136\u540e\u5c31\u53ef\u4ee5\u53bb\u505a\u66f4\u8fdb\u9636\u7684\u5185\u5bb9","title":"\u8fdb\u9636\u8def\u7ebf\u56fe"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_1","text":"\u6b64\u8def\u7ebf\u56fe\u9002\u7528\u4e8e\u5df2\u7ecf\u5b66\u8fc7\u4e86\u57fa\u7840\u673a\u5668\u5b66\u4e60 (ML, NLP, CV, RL) \u7684\u540c\u5b66 (\u9ad8\u5e74\u7ea7\u672c\u79d1\u751f\u6216\u4f4e\u5e74\u7ea7\u7814\u7a76\u751f)\uff0c\u5df2\u7ecf\u53d1\u8868\u8fc7\u81f3\u5c11\u4e00\u7bc7\u9876\u4f1a\u8bba\u6587 (NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, CVPR, ICCV) \u60f3\u8981\u8d70\u673a\u5668\u5b66\u4e60\u79d1\u7814\u8def\u7ebf\u7684\u9009\u624b\u3002 \u6b64\u8def\u7ebf\u7684\u76ee\u6807\u662f\u4e3a\u8bfb\u61c2\u4e0e\u53d1\u8868\u673a\u5668\u5b66\u4e60\u9876\u4f1a\u8bba\u6587\u6253\u4e0b\u7406\u8bba\u57fa\u7840\uff0c\u7279\u522b\u662f Probabilistic Methods \u8fd9\u4e2a track \u4e0b\u7684\u6587\u7ae0 \u673a\u5668\u5b66\u4e60\u8fdb\u9636\u53ef\u80fd\u5b58\u5728\u591a\u79cd\u4e0d\u540c\u7684\u5b66\u4e60\u8def\u7ebf\uff0c\u6b64\u8def\u7ebf\u53ea\u80fd\u4ee3\u8868\u4f5c\u8005 Yao Fu \u6240\u7406\u89e3\u7684\u6700\u4f73\u8def\u5f84\uff0c\u4fa7\u91cd\u4e8e\u8d1d\u53f6\u65af\u5b66\u6d3e\u4e0b\u7684\u6982\u7387\u5efa\u6a21\u65b9\u6cd5\uff0c\u4e5f\u4f1a\u6d89\u53ca\u5230\u5404\u9879\u76f8\u5173\u5b66\u79d1\u7684\u4ea4\u53c9\u77e5\u8bc6\u3002","title":"\u673a\u5668\u5b66\u4e60\u8fdb\u9636"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_2","text":"PRML: Pattern Recognition and Machine Learning. Christopher Bishop \u7ecf\u5178\u8d1d\u53f6\u65af\u5b66\u6d3e\u6559\u6750 AoS: All of Statistics. Larry Wasserman \u7ecf\u5178\u9891\u7387\u5b66\u6d3e\u6559\u6750 \u6240\u4ee5\u8fd9\u4e24\u672c\u4e66\u521a\u597d\u76f8\u8f85\u76f8\u6210","title":"\u5fc5\u8bfb\u6559\u6750"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_3","text":"MLAPP: Machine Learning: A Probabilistic Perspective. Kevin Murphy Convex Optimization. Stephen Boyd and Lieven Vandenberghe","title":"\u5b57\u5178"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_4","text":"W&J: Graphical Models, Exponential Families, and Variational Inference. Martin Wainwright and Michael Jordan Theory of Point Estimation. E. L. Lehmann and George Casella","title":"\u8fdb\u9636\u4e66\u7c4d"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_5","text":"","title":"\u5982\u4f55\u9605\u8bfb"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#guidelines","text":"\u5fc5\u8bfb\u6559\u6750\u5c31\u662f\u4e00\u5b9a\u8981\u8bfb\u7684\u6559\u6750 \u5b57\u5178\u7684\u610f\u601d\u662f\uff0c\u4e00\u822c\u60c5\u51b5\u4e0b\u4e0d\u7ba1\u5b83\uff0c\u4f46\u5f53\u9047\u5230\u4e86\u4e0d\u61c2\u7684\u6982\u5ff5\u7684\u65f6\u5019\uff0c\u5c31\u53bb\u5b57\u5178\u91cc\u9762\u67e5\uff08\u800c\u4e0d\u662f\u7ef4\u57fa\u767e\u79d1\uff09 \u8fdb\u9636\u4e66\u7c4d\u5148\u4e0d\u8bfb\uff0c\u5148\u8bfb\u5b8c\u5fc5\u8bfb\u4e66\u7c4d\u3002\u5fc5\u8bfb\u4e66\u7c4d\u4e00\u822c\u90fd\u662f\u8981\u524d\u524d\u540e\u540e\u53cd\u590d\u770b\u8fc7 N \u904d\u624d\u7b97\u8bfb\u5b8c \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u6700\u91cd\u8981\u7684\u8bfb\u6cd5\u5c31\u662f\u5bf9\u6bd4\u9605\u8bfb (contrastive-comparative reading)\uff1a\u540c\u65f6\u6253\u5f00\u4e24\u672c\u4e66\u8bb2\u540c\u4e00\u4e3b\u9898\u7684\u7ae0\u8282\uff0c\u7136\u540e\u5bf9\u6bd4\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u5c3d\u91cf\u53bb\u56de\u60f3\u4e4b\u524d\u8bfb\u8fc7\u7684\u8bba\u6587\uff0c\u6bd4\u8f83\u8bba\u6587\u548c\u6559\u6750\u7684\u76f8\u540c\u70b9\u4e0e\u4e0d\u540c\u70b9","title":"Guidelines"},{"location":"en/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/#_6","text":"\u5148\u8bfb AoS \u7b2c\u516d\u7ae0: Models, Statistical Inference and Learning\uff0c\u8fd9\u4e00\u90e8\u5206\u662f\u6700\u57fa\u7840\u7684\u79d1\u666e \u7136\u540e\u8bfb PRML \u7b2c 10, 11 \u7ae0 \u7b2c 10 \u7ae0\u7684\u5185\u5bb9\u662f Variational Inference, \u7b2c 11 \u7ae0\u7684\u5185\u5bb9\u662f MCMC, \u8fd9\u4e24\u79cd\u65b9\u6cd5\u662f\u8d1d\u53f6\u65af\u63a8\u65ad\u7684\u4e24\u6761\u6700\u4e3b\u8981\u8def\u7ebf \u5982\u679c\u5728\u8bfb PRML \u7684\u8fc7\u7a0b\u4e2d\u53d1\u73b0\u6709\u4efb\u4f55\u4e0d\u61c2\u7684\u540d\u8bcd\uff0c\u5c31\u53bb\u7ffb\u524d\u9762\u7684\u7ae0\u8282\u3002\u5f88\u5927\u6982\u7387\u80fd\u591f\u5728\u7b2c 3\uff0c4 \u7ae0\u627e\u5230\u76f8\u5bf9\u5e94\u7684\u5b9a\u4e49\uff1b\u5982\u679c\u627e\u4e0d\u5230\u6216\u8005\u4e0d\u591f\u8be6\u7ec6\uff0c\u5c31\u53bb\u67e5 MLAPP AoS \u7b2c 8 \u7ae0 (Parametric Inference) \u548c\u7b2c 11 \u7ae0 (Bayesian Inference) \u4e5f\u53ef\u4ee5\u4f5c\u4e3a\u53c2\u8003\u3002\u6700\u597d\u7684\u65b9\u6cd5\u662f\u591a\u672c\u4e66\u5bf9\u6bd4\u9605\u8bfb\uff0c\u6d41\u7a0b\u5982\u4e0b \u5047\u8bbe\u6211\u5728\u8bfb PRML \u7b2c 10 \u7ae0\u7684\u65f6\u5019\u53d1\u73b0\u4e86\u4e00\u4e2a\u4e0d\u61c2\u7684\u8bcd\uff1aposterior inference \u4e8e\u662f\u6211\u5f80\u524d\u7ffb\uff0c\u7ffb\u5230\u4e86\u7b2c 3 \u7ae0 (Linear Model for Regression)\uff0c\u770b\u5230\u4e86\u6700\u7b80\u5355\u7684 posterior \u7136\u540e\u6211\u63a5\u7740\u7ffb AoS\uff0c\u7ffb\u5230\u4e86\u7b2c 11 \u7ae0\uff0c\u4e5f\u6709\u5bf9 posterior \u7684\u63cf\u8ff0 \u7136\u540e\u6211\u5bf9\u6bd4 PRML \u7b2c 10 \u7ae0\uff0c\u7b2c 3 \u7ae0\uff0cAoS \u7b2c 11 \u7ae0\uff0c\u4e09\u5904\u4e0d\u540c\u5730\u65b9\u5bf9 posterior \u7684\u89e3\u8bfb\uff0c\u6bd4\u8f83\u5176\u76f8\u540c\u70b9\u548c\u4e0d\u540c\u70b9\u548c\u8054\u7cfb \u8bfb\u5b8c PRML \u7b2c 10 \u548c 11 \u7ae0\u4e4b\u540e\uff0c\u63a5\u7740\u8bfb AoS \u7b2c 24 \u7ae0 (Simulation Methods)\uff0c\u7136\u540e\u628a\u5b83\u548c PRML \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb -- \u8fd9\u4fe9\u90fd\u662f\u8bb2 MCMC \u5982\u679c\u5230\u6b64\u5904\u53d1\u73b0\u8fd8\u6709\u57fa\u7840\u6982\u5ff5\u8bfb\u4e0d\u61c2\uff0c\u5c31\u56de\u5230 PRML \u7b2c 3 \u7ae0\uff0c\u628a\u5b83\u548c AoS \u7b2c 11 \u7ae0\u5bf9\u6bd4\u9605\u8bfb Again\uff0c\u5bf9\u6bd4\u9605\u8bfb\u975e\u5e38\u91cd\u8981\uff0c\u4e00\u5b9a\u8981\u628a\u4e0d\u540c\u672c\u4e66\u7684\u7c7b\u4f3c\u5185\u5bb9\u540c\u65f6\u6446\u5728\u9762\u524d\u76f8\u4e92\u5bf9\u6bd4\uff0c\u8fd9\u6837\u53ef\u4ee5\u663e\u8457\u589e\u5f3a\u8bb0\u5fc6 \u7136\u540e\u8bfb PRML \u7b2c 13 \u7ae0\uff08\u8df3\u8fc7\u7b2c 12 \u7ae0\uff09\uff0c\u8fd9\u4e00\u7ae0\u53ef\u4ee5\u548c MLAPP \u7684\u7b2c 17, 18 \u7ae0\u5bf9\u6bd4\u9605\u8bfb MLAPP \u7b2c 17 \u7ae0\u662f PRML \u7b2c 13.2 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 HMM MLAPP \u7b2c 18 \u7ae0\u662f PRML \u7b2c 13.3 \u7ae0\u7684\u8be6\u7ec6\u7248\uff0c\u4e3b\u8981\u8bb2 LDS \u8bfb\u5b8c PRML \u7b2c 13 \u7ae0\u4e4b\u540e\uff0c\u518d\u53bb\u8bfb PRML \u7b2c 8 \u7ae0 (Graphical Models) -- \u6b64\u65f6\u8fd9\u90e8\u5206\u5e94\u8be5\u4f1a\u8bfb\u5f97\u5f88\u8f7b\u677e \u4ee5\u4e0a\u7684\u5185\u5bb9\u53ef\u4ee5\u8fdb\u4e00\u6b65\u5bf9\u7167 CMU 10-708 PGM \u8bfe\u7a0b\u6750\u6599 \u5230\u76ee\u524d\u4e3a\u6b62\uff0c\u5e94\u8be5\u80fd\u591f\u638c\u63e1 \u6982\u7387\u6a21\u578b\u7684\u57fa\u7840\u5b9a\u4e49 \u7cbe\u51c6\u63a8\u65ad - Sum-Product \u8fd1\u4f3c\u63a8\u65ad - MCMC \u8fd1\u4f3c\u63a8\u65ad - VI \u7136\u540e\u5c31\u53ef\u4ee5\u53bb\u505a\u66f4\u8fdb\u9636\u7684\u5185\u5bb9","title":"\u57fa\u7840\u8def\u5f84"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/","text":"CS224n: Natural Language Processing \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684 NLP \u5165\u95e8\u8bfe\u7a0b\uff0c\u7531\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9886\u57df\u7684\u5de8\u4f6c Chris Manning \u9886\u8854\u6559\u6388\uff08word2vec \u7b97\u6cd5\u7684\u5f00\u521b\u8005\uff09\u3002\u5185\u5bb9\u8986\u76d6\u4e86\u8bcd\u5411\u91cf\u3001RNN\u3001LSTM\u3001Seq2Seq \u6a21\u578b\u3001\u673a\u5668\u7ffb\u8bd1\u3001\u6ce8\u610f\u529b\u673a\u5236\u3001Transformer \u7b49\u7b49 NLP \u9886\u57df\u7684\u6838\u5fc3\u77e5\u8bc6\u70b9\u3002 5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\u96be\u5ea6\u5faa\u5e8f\u6e10\u8fdb\uff0c\u5206\u522b\u662f\u8bcd\u5411\u91cf\u3001word2vec \u7b97\u6cd5\u3001Dependency parsing\u3001\u673a\u5668\u7ffb\u8bd1\u4ee5\u53ca Transformer \u7684 fine-tune\u3002 \u6700\u7ec8\u7684\u5927\u4f5c\u4e1a\u662f\u5728 Stanford \u8457\u540d\u7684 SQuAD \u6570\u636e\u96c6\u4e0a\u8bad\u7ec3 QA \u6a21\u578b\uff0c\u6709\u5b66\u751f\u7684\u5927\u4f5c\u4e1a\u751a\u81f3\u76f4\u63a5\u53d1\u8868\u4e86\u9876\u4f1a\u8bba\u6587\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs224n/index.html \u8bfe\u7a0b\u89c6\u9891\uff1aB \u7ad9\u641c\u7d22 CS224n \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224n/index.html \uff0c5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a + 1 \u4e2a Final Project \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS224n - GitHub \u4e2d\u3002","title":"Stanford CS224n: Natural Language Processing"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#cs224n-natural-language-processing","text":"","title":"CS224n: Natural Language Processing"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684 NLP \u5165\u95e8\u8bfe\u7a0b\uff0c\u7531\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9886\u57df\u7684\u5de8\u4f6c Chris Manning \u9886\u8854\u6559\u6388\uff08word2vec \u7b97\u6cd5\u7684\u5f00\u521b\u8005\uff09\u3002\u5185\u5bb9\u8986\u76d6\u4e86\u8bcd\u5411\u91cf\u3001RNN\u3001LSTM\u3001Seq2Seq \u6a21\u578b\u3001\u673a\u5668\u7ffb\u8bd1\u3001\u6ce8\u610f\u529b\u673a\u5236\u3001Transformer \u7b49\u7b49 NLP \u9886\u57df\u7684\u6838\u5fc3\u77e5\u8bc6\u70b9\u3002 5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\u96be\u5ea6\u5faa\u5e8f\u6e10\u8fdb\uff0c\u5206\u522b\u662f\u8bcd\u5411\u91cf\u3001word2vec \u7b97\u6cd5\u3001Dependency parsing\u3001\u673a\u5668\u7ffb\u8bd1\u4ee5\u53ca Transformer \u7684 fine-tune\u3002 \u6700\u7ec8\u7684\u5927\u4f5c\u4e1a\u662f\u5728 Stanford \u8457\u540d\u7684 SQuAD \u6570\u636e\u96c6\u4e0a\u8bad\u7ec3 QA \u6a21\u578b\uff0c\u6709\u5b66\u751f\u7684\u5927\u4f5c\u4e1a\u751a\u81f3\u76f4\u63a5\u53d1\u8868\u4e86\u9876\u4f1a\u8bba\u6587\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs224n/index.html \u8bfe\u7a0b\u89c6\u9891\uff1aB \u7ad9\u641c\u7d22 CS224n \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224n/index.html \uff0c5 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a + 1 \u4e2a Final Project","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224n/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS224n - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/","text":"CS224w: Machine Learning with Graphs \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython, LaTeX \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684\u56fe\u795e\u7ecf\u7f51\u7edc\u5165\u95e8\u8bfe\uff0c\u8fd9\u95e8\u8bfe\u6211\u6ca1\u6709\u4e0a\u8fc7\uff0c\u4f46\u4f17\u591a\u505a GNN \u7684\u670b\u53cb\u90fd\u5411\u6211\u529b\u8350\u8fc7\u8fd9\u95e8\u8bfe\uff0c\u60f3\u5fc5 Stanford \u7684\u8bfe\u8d28\u91cf\u8fd8\u662f\u4e00\u5982\u65e2\u5f80\u5730\u6709\u4fdd\u8bc1\u7684\u3002\u53e6\u5916\u5c31\u662f\u8fd9\u95e8\u8bfe\u7684\u6388\u8bfe\u8001\u5e08\u975e\u5e38\u5e74\u8f7b\u5e05\u6c14:) \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs224w/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/watch?v=JAB_plj2rbA \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224w/ \uff0c6 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\uff0c3 \u4e2a LaTeX \u4e66\u9762\u4f5c\u4e1a","title":"Stanford CS224w: Machine Learning with Graphs"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/#cs224w-machine-learning-with-graphs","text":"","title":"CS224w: Machine Learning with Graphs"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython, LaTeX \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684\u56fe\u795e\u7ecf\u7f51\u7edc\u5165\u95e8\u8bfe\uff0c\u8fd9\u95e8\u8bfe\u6211\u6ca1\u6709\u4e0a\u8fc7\uff0c\u4f46\u4f17\u591a\u505a GNN \u7684\u670b\u53cb\u90fd\u5411\u6211\u529b\u8350\u8fc7\u8fd9\u95e8\u8bfe\uff0c\u60f3\u5fc5 Stanford \u7684\u8bfe\u8d28\u91cf\u8fd8\u662f\u4e00\u5982\u65e2\u5f80\u5730\u6709\u4fdd\u8bc1\u7684\u3002\u53e6\u5916\u5c31\u662f\u8fd9\u95e8\u8bfe\u7684\u6388\u8bfe\u8001\u5e08\u975e\u5e38\u5e74\u8f7b\u5e05\u6c14:)","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS224w/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs224w/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.youtube.com/watch?v=JAB_plj2rbA \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a http://web.stanford.edu/class/cs224w/ \uff0c6 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a\uff0c3 \u4e2a LaTeX \u4e66\u9762\u4f5c\u4e1a","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS230/","text":"Coursera: Deep Learning \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u673a\u5668\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 \u5434\u6069\u8fbe\u5728 Coursera \u5f00\u8bbe\u7684\u53e6\u4e00\u95e8\u7f51\u7ea2\u8bfe\u7a0b\uff0c\u5b66\u4e60\u8005\u65e0\u6570\uff0c\u582a\u79f0\u5723\u7ecf\u7ea7\u7684\u6df1\u5ea6\u5b66\u4e60\u5165\u95e8\u8bfe\u3002\u6df1\u5165\u6d45\u51fa\u7684\u8bb2\u89e3\uff0c\u773c\u82b1\u7f2d\u4e71\u7684 Project\u3002\u4ece\u6700\u57fa\u7840\u7684\u795e\u7ecf\u7f51\u7edc\uff0c\u5230 CNN, RNN\uff0c\u518d\u5230\u6700\u8fd1\u5927\u70ed\u7684 Transformer\u3002\u5b66\u5b8c\u8fd9\u95e8\u8bfe\uff0c\u4f60\u5c06\u521d\u6b65\u638c\u63e1\u6df1\u5ea6\u5b66\u4e60\u9886\u57df\u5fc5\u5907\u7684\u77e5\u8bc6\u548c\u6280\u80fd\uff0c\u5e76\u4e14\u53ef\u4ee5\u5728 Kaggle \u4e2d\u53c2\u52a0\u81ea\u5df1\u611f\u5174\u8da3\u7684\u6bd4\u8d5b\uff0c\u5728\u5b9e\u8df5\u4e2d\u953b\u70bc\u81ea\u5df1\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.coursera.org/specializations/deep-learning \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.coursera.org/specializations/deep-learning \uff0cB\u7ad9\u6709\u642c\u8fd0 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://www.coursera.org/specializations/deep-learning","title":"Coursera: Deep Learning"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS230/#coursera-deep-learning","text":"","title":"Coursera: Deep Learning"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS230/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u673a\u5668\u5b66\u4e60\u57fa\u7840 + Python \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 \u5434\u6069\u8fbe\u5728 Coursera 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https://www.coursera.org/specializations/deep-learning \uff0cB\u7ad9\u6709\u642c\u8fd0 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://www.coursera.org/specializations/deep-learning","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS231/","text":"CS231n: CNN for Visual Recognition \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u673a\u5668\u5b66\u4e60\u57fa\u7840 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a80 \u5c0f\u65f6 Stanford \u7684 CV \u5165\u95e8\u8bfe\uff0c\u7531\u8ba1\u7b97\u673a\u9886\u57df\u7684\u5de8\u4f6c\u674e\u98de\u98de\u9662\u58eb\u9886\u8854\u6559\u6388\uff08CV \u9886\u57df\u5212\u65f6\u4ee3\u7684\u8457\u540d\u6570\u636e\u96c6 ImageNet 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Regression\u3001Classification\u3001CNN\u3001Self-Attention\u3001Transformer\u3001GAN\u3001BERT\u3001Anomaly Detection\u3001Explainable AI\u3001Attack\u3001Adaptation\u3001 RL\u3001Compression\u3001Life-Long Learning \u4ee5\u53ca Meta Learning\u3002\u53ef\u8c13\u662f\u5305\u7f57\u4e07\u8c61\uff0c\u80fd\u8ba9\u5b66\u751f\u5bf9\u4e8e\u6df1\u5ea6\u5b66\u4e60\u7684\u7edd\u5927\u591a\u6570\u9886\u57df\u90fd\u6709\u4e00\u5b9a\u4e86\u89e3\uff0c\u4ece\u800c\u53ef\u4ee5\u8fdb\u4e00\u6b65\u9009\u62e9\u60f3\u8981\u6df1\u5165\u7684\u65b9\u5411\u8fdb\u884c\u5b66\u4e60\u3002 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\uff0c\u6bcf\u8282\u8bfe\u7684\u94fe\u63a5\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a https://speech.ee.ntu.edu.tw/~hylee/ml/2021-spring.html \uff0c15 \u4e2a lab\uff0c\u51e0\u4e4e\u8986\u76d6\u4e86\u4e3b\u6d41\u6df1\u5ea6\u5b66\u4e60\u7684\u6240\u6709\u9886\u57df","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/EE16/","text":"UCB EE16A&B: Designing Information Devices and Systems I&II Descriptions Offered by: UC Berkeley Prerequisites: None Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 150 hours This introductory class for freshmen majoring in electronics at UC Berkeley teaches the fundamentals of circuitry. 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Course Resources Course Website: EE16A , EE16B Recordings: EE16A , EE16B Textbooks: EE16A , EE16B Assignments: EE16A , EE16B Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/EE16A - GitHub .","title":"EE16A&B: Designing Information Devices and Systems I&II"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/EE16/#ucb-ee16ab-designing-information-devices-and-systems-iii","text":"","title":"UCB EE16A&B: Designing Information Devices and Systems I&II"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/EE16/#descriptions","text":"Offered by: UC Berkeley Prerequisites: None Programming Languages: Python Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 150 hours This introductory class for freshmen majoring in electronics at UC Berkeley teaches the fundamentals of circuitry. Through a variety of hands-on labs, students will experience collecting information from the environment through sensors and analyzing it to make predictions. 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Oppenheim Okay, enough reason to take this class.","title":"Descriptions"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/Signals_and_Systems_AVO/#course-resources","text":"Course Website: https://ocw.mit.edu/resources/res-6-007-signals-and-systems-spring-2011/index.htm Recordings: https://www.bilibili.com/video/BV1CZ4y1j7hs Textbooks: Signals and Systems, 2nd Edition Assignments: https://ocw.mit.edu/resources/res-6-007-signals-and-systems-spring-2011/assignments","title":"Course Resources"},{"location":"en/%E7%94%B5%E5%AD%90%E5%9F%BA%E7%A1%80/signal/","text":"UCB EE120: Signal and Systems \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A, CS70\uff0c\u5fae\u79ef\u5206\uff0c\u7ebf\u6027\u4ee3\u6570 \u7f16\u7a0b\u8bed\u8a00\uff1aPython \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u6700\u7cbe\u534e\u7684\u90e8\u5206\u5c31\u662f 6 \u4e2a\u8d85\u6709\u8da3\u7684\u7f16\u7a0b\u4f5c\u4e1a\u4e86\uff0c\u4f1a\u8ba9\u4f60\u7528 Python \u901a\u8fc7\u5b66\u4e60\u5230\u7684\u4fe1\u53f7\u4e0e\u7cfb\u7edf\u7684\u7406\u8bba\u77e5\u8bc6\uff0c\u89e3\u51b3\u5404\u7c7b\u5b9e\u9645\u95ee\u9898\u3002\u4f8b\u5982 lab3 \u4f1a\u8ba9\u4f60\u5b9e\u73b0 FFT \u7b97\u6cd5\uff0c\u5e76\u548c Numpy \u7684\u5b98\u65b9\u5b9e\u73b0\u8fdb\u884c\u6027\u80fd\u5bf9\u6bd4\uff1blab4 \u4f1a\u901a\u8fc7\u5206\u6790\u624b\u6307\u5934\u7684\u5f71\u50cf\u6570\u636e\u63a8\u65ad\u5fc3\u7387\uff1blab5 \u5c31\u66f4\u725b\u4e86\uff0c\u4f1a\u8ba9\u4f60\u7ed9\u54c8\u52c3\u671b\u8fdc\u955c\u62cd\u5230\u7684\u7167\u7247\u8fdb\u884c\u964d\u566a\u5904\u7406\uff0c\u6062\u590d\u7eda\u70c2\u6e05\u6670\u7684\u661f\u7a7a\uff1blab6 \u4f1a\u8ba9\u4f60\u6784\u9020\u4e00\u4e2a\u53cd\u9988\u7cfb\u7edf\uff0c\u5e73\u8861\u5c0f\u8f66\u4e0a\u7684\u7ec6\u6746\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://inst.eecs.berkeley.edu/~ee120/fa19/ \u8bfe\u7a0b\u6559\u6750\uff1a\u53c2\u89c1\u8bfe\u7a0b notes \u8bfe\u7a0b\u4f5c\u4e1a\uff1a5 \u4e2a\u4e66\u9762\u4f5c\u4e1a + 6 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/UCB-EE120 - 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GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E7%A8%8B%E5%BA%8F%E8%AF%AD%E8%A8%80%E8%AE%BE%E8%AE%A1/CS242/","text":"","title":"CS242"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/","text":"UCB CS161: Computer Security \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A, CS61B, CS61C \u7f16\u7a0b\u8bed\u8a00\uff1aC, Go \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 \u4f2f\u514b\u5229\u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u8bfe\u7a0b\u5185\u5bb9\u5206\u4e3a5\u4e2a\u90e8\u5206\uff1a Security principles: how to design a secure system Memory safety: buffer overflow attack Cryptography: symmetric encryption, asymmetric encryption, MAC, digital signature ......... Web: SQL-injection, XSS, XSRF ....... Networking: attacks for each layer \u8fd9\u95e8\u8bfe\u8ba9\u6211\u5370\u8c61\u6700\u4e3a\u6df1\u523b\u7684\u90e8\u5206\u662f Project2\uff0c\u8ba9\u4f60\u7528 Go \u8bed\u8a00\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7684\u6587\u4ef6\u5206\u4eab\u7cfb\u7edf\u3002\u6211\u82b1\u4e86\u6574\u6574\u4e09\u5929\u624d\u5b8c\u6210\u4e86\u8fd9\u4e2a\u975e\u5e38\u8650\u7684 Project\uff0c\u603b\u4ee3\u7801\u91cf\u8d85\u8fc7 3k \u884c\u3002\u5728\u8fd9\u6837\u5bc6\u96c6\u578b\u7684\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u80fd\u6781\u5927\u5730\u953b\u70bc\u4f60\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7cfb\u7edf\u7684\u80fd\u529b\u3002 2020 \u5e74\u590f\u5b63\u5b66\u671f\u7684\u7248\u672c\u5f00\u6e90\u4e86\u8bfe\u7a0b\u5f55\u5f71\uff0c\u5927\u5bb6\u53ef\u4ee5\u5728\u4e0b\u9762\u7684\u8bfe\u7a0b\u7f51\u7ad9\u94fe\u63a5\u91cc\u627e\u5230\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://su20.cs161.org/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a https://textbook.cs161.org/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a7 \u4e2a\u5728\u7ebf HW + 3 \u4e2a Lab + 3 \u4e2a Project \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/UCB-CS161 - GitHub \u4e2d\u3002","title":"UCB CS161: Computer Security"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#ucb-cs161-computer-security","text":"","title":"UCB CS161: Computer Security"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1aCS61A, CS61B, CS61C \u7f16\u7a0b\u8bed\u8a00\uff1aC, Go \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 \u4f2f\u514b\u5229\u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u8bfe\u7a0b\u5185\u5bb9\u5206\u4e3a5\u4e2a\u90e8\u5206\uff1a Security principles: how to design a secure system Memory safety: buffer overflow attack Cryptography: symmetric encryption, asymmetric encryption, MAC, digital signature ......... Web: SQL-injection, XSS, XSRF ....... Networking: attacks for each layer \u8fd9\u95e8\u8bfe\u8ba9\u6211\u5370\u8c61\u6700\u4e3a\u6df1\u523b\u7684\u90e8\u5206\u662f Project2\uff0c\u8ba9\u4f60\u7528 Go \u8bed\u8a00\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7684\u6587\u4ef6\u5206\u4eab\u7cfb\u7edf\u3002\u6211\u82b1\u4e86\u6574\u6574\u4e09\u5929\u624d\u5b8c\u6210\u4e86\u8fd9\u4e2a\u975e\u5e38\u8650\u7684 Project\uff0c\u603b\u4ee3\u7801\u91cf\u8d85\u8fc7 3k \u884c\u3002\u5728\u8fd9\u6837\u5bc6\u96c6\u578b\u7684\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u80fd\u6781\u5927\u5730\u953b\u70bc\u4f60\u8bbe\u8ba1\u548c\u5b9e\u73b0\u4e00\u4e2a\u5b89\u5168\u7cfb\u7edf\u7684\u80fd\u529b\u3002 2020 \u5e74\u590f\u5b63\u5b66\u671f\u7684\u7248\u672c\u5f00\u6e90\u4e86\u8bfe\u7a0b\u5f55\u5f71\uff0c\u5927\u5bb6\u53ef\u4ee5\u5728\u4e0b\u9762\u7684\u8bfe\u7a0b\u7f51\u7ad9\u94fe\u63a5\u91cc\u627e\u5230\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://su20.cs161.org/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a https://textbook.cs161.org/ \u8bfe\u7a0b\u4f5c\u4e1a\uff1a7 \u4e2a\u5728\u7ebf HW + 3 \u4e2a Lab + 3 \u4e2a Project","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/CS161/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/UCB-CS161 - GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/","text":"MIT 6.858: Computer System Security \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\uff0c\u5bf9\u8ba1\u7b97\u673a\u7cfb\u7edf\u6709\u521d\u6b65\u4e86\u89e3 \u7f16\u7a0b\u8bed\u8a00\uff1aC, Python \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 MIT \u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u5b9e\u9a8c\u73af\u5883\u662f\u4e00\u4e2a Web Application Zoobar\u3002\u5b66\u751f\u5b66\u4e60\u653b\u9632\u6280\u672f\u5e76\u5e94\u7528\u4e8e\u8be5 Web Application\u3002 Lab 1: you will explore the zoobar web application, and use buffer overflow attacks to break its security properties. Lab 2: you will improve the zoobar web application by using privilege separation, so that if one component is compromised, the adversary doesn't get control over the whole web application. Lab 3: you will build a program analysis tool based on symbolic execution to find bugs in Python code such as the zoobar web application. Lab 4: you will improve the zoobar application against browser attacks. \u8fd9\u4e2a\u8bfe\u6211\u4e3b\u8981\u662f\u505a\u4e86 Lab 3\u3002Lab 3 \u662f\u901a\u8fc7\u6df7\u5408\u7b26\u53f7\u6267\u884c\u6765\u904d\u5386\u7a0b\u5e8f\u7684\u6240\u6709\u5206\u652f\uff0c\u7406\u89e3\u4e86\u7b26\u53f7\u6267\u884c\u7684\u601d\u60f3\u540e Lab \u5e76\u4e0d\u96be\u505a\u3002\u8fd9\u4e2a Lab \u76f4\u89c2\u5c55\u793a\u7b26\u53f7\u6267\u884c\u8fd9\u79cd\u6280\u672f\u7684\u4f7f\u7528\u65b9\u6cd5\u3002 \u8fd9\u4e2a\u8bfe\u7684 Final Project \u662f\u5b9e\u73b0 SecFS \uff0c\u4e00\u4e2a\u8fdc\u7aef\u6587\u4ef6\u7cfb\u7edf\uff0c\u9762\u5bf9\u5b8c\u5168\u4e0d\u53ef\u4fe1\u7684\u670d\u52a1\u5668\uff0c\u63d0\u4f9b\u673a\u5bc6\u6027\u548c\u5b8c\u6574\u6027\u3002\u53c2\u8003\u8bba\u6587\u4e3a SUNDR \u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://css.csail.mit.edu/6.858/2022/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a4 \u4e2a Lab + Final Project / Lab5","title":"MIT 6.858: Computer System Security"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/#mit-6858-computer-system-security","text":"","title":"MIT 6.858: Computer System Security"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784\uff0c\u5bf9\u8ba1\u7b97\u673a\u7cfb\u7edf\u6709\u521d\u6b65\u4e86\u89e3 \u7f16\u7a0b\u8bed\u8a00\uff1aC, Python \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 MIT \u7684\u8ba1\u7b97\u673a\u7cfb\u7edf\u5b89\u5168\u8bfe\u7a0b\uff0c\u5b9e\u9a8c\u73af\u5883\u662f\u4e00\u4e2a Web Application Zoobar\u3002\u5b66\u751f\u5b66\u4e60\u653b\u9632\u6280\u672f\u5e76\u5e94\u7528\u4e8e\u8be5 Web Application\u3002 Lab 1: you will explore the zoobar web application, and use buffer overflow attacks to break its security properties. Lab 2: you will improve the zoobar web application by using privilege separation, so that if one component is compromised, the adversary doesn't get control over the whole web application. Lab 3: you will build a program analysis tool based on symbolic execution to find bugs in Python code such as the zoobar web application. Lab 4: you will improve the zoobar application against browser attacks. \u8fd9\u4e2a\u8bfe\u6211\u4e3b\u8981\u662f\u505a\u4e86 Lab 3\u3002Lab 3 \u662f\u901a\u8fc7\u6df7\u5408\u7b26\u53f7\u6267\u884c\u6765\u904d\u5386\u7a0b\u5e8f\u7684\u6240\u6709\u5206\u652f\uff0c\u7406\u89e3\u4e86\u7b26\u53f7\u6267\u884c\u7684\u601d\u60f3\u540e Lab \u5e76\u4e0d\u96be\u505a\u3002\u8fd9\u4e2a Lab \u76f4\u89c2\u5c55\u793a\u7b26\u53f7\u6267\u884c\u8fd9\u79cd\u6280\u672f\u7684\u4f7f\u7528\u65b9\u6cd5\u3002 \u8fd9\u4e2a\u8bfe\u7684 Final Project \u662f\u5b9e\u73b0 SecFS \uff0c\u4e00\u4e2a\u8fdc\u7aef\u6587\u4ef6\u7cfb\u7edf\uff0c\u9762\u5bf9\u5b8c\u5168\u4e0d\u53ef\u4fe1\u7684\u670d\u52a1\u5668\uff0c\u63d0\u4f9b\u673a\u5bc6\u6027\u548c\u5b8c\u6574\u6027\u3002\u53c2\u8003\u8bba\u6587\u4e3a SUNDR \u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E7%B3%BB%E7%BB%9F%E5%AE%89%E5%85%A8/MIT6.858/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://css.csail.mit.edu/6.858/2022/ \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9 \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a4 \u4e2a Lab + Final Project / Lab5","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/","text":"Stanford CS106B/X: Programming Abstractions in C++ Descriptions Offered by: Stanford Prerequisites: CS50/CS106A/CS61A or equivalent Programming Languages: C++ Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 50-70 hours CS106B/X are advanced programming courses at Stanford. CS106X is more difficult and in-depth than CS106B, but the main content is similar. Based on programming assignments in C++ language, students will develop the ability to solve real-world problems through programming abstraction. It also covers some simple data structures and algorithms, but is generally not as systematic as a specialized data structures course. Resources Course Website: CS106B , CS106X Textbook: https://web.stanford.edu/class/cs106x/res/reader/CS106BX-Reader.pdf Recordings: https://www.bilibili.com/video/BV1G7411k7jG","title":"Stanford CS106B/X"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/#stanford-cs106bx-programming-abstractions-in-c","text":"","title":"Stanford CS106B/X: Programming Abstractions in C++"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/#descriptions","text":"Offered by: Stanford Prerequisites: CS50/CS106A/CS61A or equivalent Programming Languages: C++ Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 50-70 hours CS106B/X are advanced programming courses at Stanford. CS106X is more difficult and in-depth than CS106B, but the main content is similar. Based on programming assignments in C++ language, students will develop the ability to solve real-world problems through programming abstraction. It also covers some simple data structures and algorithms, but is generally not as systematic as a specialized data structures course.","title":"Descriptions"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106B_CS106X/#resources","text":"Course Website: CS106B , CS106X Textbook: https://web.stanford.edu/class/cs106x/res/reader/CS106BX-Reader.pdf Recordings: https://www.bilibili.com/video/BV1G7411k7jG","title":"Resources"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/","text":"CS106L: Stanford C++ Programming Descriptions Offered by: Stanford Prerequisites: better if you are already proficient in a programming language Programming Languages: C++ Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 20 hours I've been writing C++ code since freshman year, and it wasn't until I finished this class that I realized the C++ code I was writing was probably just C + cin / cout . This class will dive into a lot of standard C++ features and syntax that will allow you to write quality C++ code. Techniques such as auto binding, uniform initialization, lambda function, move semantics, RAII, etc. have been used repeatedly in my coding career since then and are very useful. It is worth mentioning that in this class, you will implement a HashMap (similar to unordered_map in STL), which almost ties the whole course together and is a great test of coding skills. Especially after the implementation of iterator , I started to understand why Linus is so sarcastic about C/C++, because it is really hard to write correctly. In short, the course is not difficult but very informative which requires you to consolidate repeatedly in later practice. The reason why Stanford offers a single C++ programming class is that many of the subsequent CS courses' projects are based on C++. For example, CS144 Computer Networks and CS143 Compilers. Both of these courses are included in this book. Course Resources Course Website: http://web.stanford.edu/class/cs106l/ Recordings: https://www.youtube.com/channel/UCSqr6y-eaQT_qZJVUm_4QxQ/playlists Textbook: http://web.stanford.edu/class/cs106l/full_course_reader.pdf Assignment1 Download: https://github.com/snme/cs106L-assignment1 Assignment2 Download: https://github.com/snme/cs106L-assignment2 Assignments: refer to course website Personal Resources All the resources and assignments used by @PKUFlyingPig are maintained in PKUFlyingPig/CS106L - GitHub .","title":"Stanford CS106L: Standard C++ Programming"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#cs106l-stanford-c-programming","text":"","title":"CS106L: Stanford C++ Programming"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#descriptions","text":"Offered by: Stanford Prerequisites: better if you are already proficient in a programming language Programming Languages: C++ Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 20 hours I've been writing C++ code since freshman year, and it wasn't until I finished this class that I realized the C++ code I was writing was probably just C + cin / cout . This class will dive into a lot of standard C++ features and syntax that will allow you to write quality C++ code. Techniques such as auto binding, uniform initialization, lambda function, move semantics, RAII, etc. have been used repeatedly in my coding career since then and are very useful. It is worth mentioning that in this class, you will implement a HashMap (similar to unordered_map in STL), which almost ties the whole course together and is a great test of coding skills. Especially after the implementation of iterator , I started to understand why Linus is so sarcastic about C/C++, because it is really hard to write correctly. In short, the course is not difficult but very informative which requires you to consolidate repeatedly in later practice. The reason why Stanford offers a single C++ programming class is that many of the subsequent CS courses' projects are based on C++. For example, CS144 Computer Networks and CS143 Compilers. Both of these courses are included in this book.","title":"Descriptions"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#course-resources","text":"Course Website: http://web.stanford.edu/class/cs106l/ Recordings: https://www.youtube.com/channel/UCSqr6y-eaQT_qZJVUm_4QxQ/playlists Textbook: http://web.stanford.edu/class/cs106l/full_course_reader.pdf Assignment1 Download: https://github.com/snme/cs106L-assignment1 Assignment2 Download: https://github.com/snme/cs106L-assignment2 Assignments: refer to course website","title":"Course Resources"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS106L/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig are maintained in PKUFlyingPig/CS106L - GitHub .","title":"Personal Resources"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS110L/","text":"CS110L: Safety in Systems Programming \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u6700\u597d\u6709\u4e00\u5b9a\u7684\u7f16\u7a0b\u80cc\u666f\u5e76\u5bf9\u8ba1\u7b97\u673a\u7cfb\u7edf\u6709\u521d\u6b65\u7684\u8ba4\u8bc6\u3002 \u7f16\u7a0b\u8bed\u8a00\uff1aRust \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a30 \u5c0f\u65f6 \u5728\u8fd9\u95e8\u8bfe\u4e2d\u4f60\u5c06\u4f1a\u5b66\u4e60 Rust \u8fd9\u95e8\u795e\u5947\u7684\u8bed\u8a00\u3002 \u5982\u679c\u4f60\u5b66\u8fc7 C \u5e76\u63a5\u89e6\u8fc7\u4e00\u4e9b\u7cfb\u7edf\u7f16\u7a0b\u7684\u8bdd\uff0c\u5e94\u8be5\u5bf9 C \u7684\u5185\u5b58\u6cc4\u6f0f\u4ee5\u53ca\u6307\u9488\u7684\u5371\u9669\u6709\u6240\u8033\u95fb\uff0c\u4f46 C \u7684\u5e95\u5c42\u7279\u6027\u4ee5\u53ca\u9ad8\u6548\u4ecd\u7136\u8ba9\u5b83\u5728\u7cfb\u7edf\u7ea7\u7f16\u7a0b\u4e2d\u65e0\u6cd5\u88ab\u4f8b\u5982 Java \u7b49\u81ea\u5e26\u5783\u573e\u6536\u96c6\u673a\u5236\u7684\u9ad8\u7ea7\u8bed\u8a00\u6240\u66ff\u4ee3\u3002\u800c Rust \u7684\u76ee\u6807\u5219\u662f\u5e0c\u671b\u5728 C \u7684\u9ad8\u6548\u57fa\u7840\u4e0a\uff0c\u5f25\u8865\u5176\u5b89\u5168\u4e0d\u8db3\u7684\u7f3a\u70b9\u3002\u56e0\u6b64 Rust \u5728\u8bbe\u8ba1\u4e4b\u521d\uff0c\u5c31\u6709\u5e26\u6709\u5f88\u591a\u7cfb\u7edf\u7f16\u7a0b\u7684\u89c2\u70b9\u3002\u5b66\u4e60 Rust\uff0c\u4e5f\u80fd\u8ba9\u4f60\u4e4b\u540e\u80fd\u7528 C \u8bed\u8a00\u7f16\u5199\u51fa\u66f4\u5b89\u5168\u66f4\u4f18\u96c5\u7684\u7cfb\u7edf\u7ea7\u4ee3\u7801\uff08\u4f8b\u5982\u64cd\u4f5c\u7cfb\u7edf\u7b49\uff09\u3002 \u8fd9\u95e8\u8bfe\u7684\u540e\u534a\u90e8\u5206\u5173\u6ce8\u5728\u5e76\u53d1\uff08concurrency\uff09\u8fd9\u4e00\u4e3b\u9898\u4e0a\uff0c\u4f60\u5c06\u4f1a\u7cfb\u7edf\u5730\u638c\u63e1\u591a\u8fdb\u7a0b\u3001\u591a\u7ebf\u7a0b\u3001\u57fa\u4e8e\u4e8b\u4ef6\u9a71\u52a8\u7684\u5e76\u53d1\u7b49\u82e5\u5e72\u5e76\u53d1\u6280\u672f\uff0c\u5e76\u5728\u7b2c\u4e8c\u4e2a Project \u4e2d\u6bd4\u8f83\u5b83\u4eec\u5404\u81ea\u7684\u4f18\u52a3\u3002Rust \u4e2d \u201cfutures\u201d \u7684\u6982\u5ff5\u975e\u5e38\u6709\u8da3\u548c\u4f18\u96c5\uff0c\u8fd9\u4e9b\u57fa\u7840\u77e5\u8bc6\u5bf9\u4f60\u540e\u7eed\u5bf9\u8ba1\u7b97\u673a\u7cfb\u7edf\u76f8\u5173\u8bfe\u7a0b\u7684\u5b66\u4e60\u5f88\u6709\u5e2e\u52a9\u3002\u53e6\u5916\uff0c\u6e05\u534e\u5927\u5b66\u7684\u64cd\u7edf\u5b9e\u9a8c rCore \u5c31\u662f\u57fa\u4e8e Rust \u7f16\u5199\u7684\uff0c\u5177\u4f53\u53c2\u89c1 \u6587\u6863 \u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a https://reberhardt.com/cs110l/spring-2020/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://youtu.be/j7AQrtLevUE \u8bfe\u7a0b\u6559\u6750\uff1a\u65e0 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u5171 6 \u4e2a Lab \u548c 2 \u4e2a Project\uff0c\u4f5c\u4e1a\u6587\u6863\u548c\u4ee3\u7801\u6846\u67b6\u8be6\u89c1\u8bfe\u7a0b\u7f51\u7ad9\u3002\u5176\u4e2d\u4e24\u4e2a Project \u975e\u5e38\u6709\u8da3\uff0c\u5206\u522b\u662f\uff1a \u7528 Rust \u5b9e\u73b0\u4e00\u4e2a\u7c7b\u4f3c\u4e8e GDB \u7684 debugger \u7528 Rust \u5b9e\u73b0\u4e00\u4e2a\u8d1f\u8f7d\u5747\u8861\u5668 \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS110L - 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GitHub \u4e2d\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS50/","text":"CS50: This is CS50x Descriptions Offered by: Harvard Prerequisites: None Programming Languages: C, Python, SQL, HTML, CSS, JavaScript Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 20 hours This course has been voted the most popular public course by Harvard students for many years. Professor Malan is very passionate in class. I still remember the scene where he tears up the Yellow pages to explain the dichotomy method. Since this is a university-wide public course, the contents are pretty friendly to beginners and even if you already have some programming experience, all the programming assignments are quite exciting and worth a try. Course Resources Course Website: https://cs50.harvard.edu/x/2022/ Recordings: https://cs50.harvard.edu/x/2022/ Assignments: https://cs50.harvard.edu/x/2022/","title":"Harvard CS50: This is CS50x"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS50/#cs50-this-is-cs50x","text":"","title":"CS50: This is CS50x"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS50/#descriptions","text":"Offered by: Harvard Prerequisites: None Programming Languages: C, Python, SQL, HTML, CSS, JavaScript Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 20 hours This course has been voted the most popular public course by Harvard students for many years. Professor Malan is very passionate in class. I still remember the scene where he tears up the Yellow pages to explain the dichotomy method. 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The CS61 series is composed of introductory courses to the CS major at Berkeley, where CS61A: Emphasizes abstraction and equips students to use programs to solve real-world problems without focusing on the underlying hardware details. CS61B: Focuses on algorithms and data structures and the construction of large-scale programs, where students combine knowledge of algorithms and data structures with the Java language to build large-scale projects at the thousand-line code level (such as a simple Google Maps, a two-dimensional version of Minecraft). CS61C: Focusing on computer architecture, students will understand how high-level languages (e.g. C) are converted step-by-step into machine-understandable bit strings and executed on CPUs. Students will learn about the RISC-V architecture and implement a CPU on their own by using Logism. CS61B and CS61C are both included in this guidebook. Going back to CS61A, you will note that this is not just a programming language class, but goes deeper into the principles of program construction and operation. Finally you will implement an interpreter for Scheme in Python in Project 4. In addition, abstraction will be a major theme in this class, as you will learn about functional programming, data abstraction, object orientation, etc. to make your code more readable and modular. Of course, learning a programming language is also a big part of this course. You will master three programming languages, Python, Scheme, and SQL, and in learning and comparing them, you will be equiped with the ability to quickly master a new programming language. Note: If you have no prior programming experience at all, getting started with CS61A requires a relatively high level of learning ability and self-discipline. To avoid the frustration of a struggling experience, you may choose a more friendly introductory programming course at first. For example, CS10 at Berkeley or CS50 at Harvard are both good choices. Course Resources Course Website: https://inst.eecs.berkeley.edu/~cs61a/su20/ Recordings: refer to the course website Textbook: http://composingprograms.com/ Assignments: refer to the course website Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPic/CS61A - GitHub","title":"UCB CS61A: Structure and Interpretation of Computer Programs"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS61A/#cs61a-structure-and-interpretation-of-computer-programs","text":"","title":"CS61A: Structure and Interpretation of Computer Programs"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS61A/#descriptions","text":"Offered by: UC Berkeley Prerequisites: None Programming Languages: Python, Scheme, SQL Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 50 hours This is the first course in the Berkeley CS61 series, and my introductory course to Python. The CS61 series is composed of introductory courses to the CS major at Berkeley, where CS61A: Emphasizes abstraction and equips students to use programs to solve real-world problems without focusing on the underlying hardware details. CS61B: Focuses on algorithms and data structures and the construction of large-scale programs, where students combine knowledge of algorithms and data structures with the Java language to build large-scale projects at the thousand-line code level (such as a simple Google Maps, a two-dimensional version of Minecraft). CS61C: Focusing on computer architecture, students will understand how high-level languages (e.g. C) are converted step-by-step into machine-understandable bit strings and executed on CPUs. Students will learn about the RISC-V architecture and implement a CPU on their own by using Logism. CS61B and CS61C are both included in this guidebook. Going back to CS61A, you will note that this is not just a programming language class, but goes deeper into the principles of program construction and operation. Finally you will implement an interpreter for Scheme in Python in Project 4. In addition, abstraction will be a major theme in this class, as you will learn about functional programming, data abstraction, object orientation, etc. to make your code more readable and modular. Of course, learning a programming language is also a big part of this course. You will master three programming languages, Python, Scheme, and SQL, and in learning and comparing them, you will be equiped with the ability to quickly master a new programming language. Note: If you have no prior programming experience at all, getting started with CS61A requires a relatively high level of learning ability and self-discipline. To avoid the frustration of a struggling experience, you may choose a more friendly introductory programming course at first. For example, CS10 at Berkeley or CS50 at Harvard are both good choices.","title":"Descriptions"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS61A/#course-resources","text":"Course Website: https://inst.eecs.berkeley.edu/~cs61a/su20/ Recordings: refer to the course website Textbook: http://composingprograms.com/ Assignments: refer to the course website","title":"Course Resources"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/CS61A/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPic/CS61A - GitHub","title":"Personal Resources"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/Duke-Coursera-Intro-C/","text":"Introductory C Programming Specialization \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aDuke \u5148\u4fee\u8981\u6c42\uff1a\u65e0 \u7f16\u7a0b\u8bed\u8a00\uff1aC \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a110 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Specialization"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/Duke-Coursera-Intro-C/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aDuke \u5148\u4fee\u8981\u6c42\uff1a\u65e0 \u7f16\u7a0b\u8bed\u8a00\uff1aC \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a110 \u5c0f\u65f6 \u975e\u5e38\u597d\u7684\u8bfe\u7a0b\uff0c\u81ea\u6211\u611f\u89c9\u6536\u76ca\u975e\u5e38\u5927\uff1a \u4fa7\u91cd\u57fa\u7840\u548c\u57fa\u672c\u6982\u5ff5\uff1a\u5982 frame\u3001stack memory\u3001heap memory \u7b49\u8bb2\u5f97\u5f88\u900f\u3002 \u9488\u5bf9C\u6700\u96be\u638c\u63e1\u7684\u6307\u9488\uff0c\u6709\u597d\u7684\u7ec3\u4e60\u548c\u7f16\u7a0b\u6765\u52a0\u6df1\u548c\u5f3a\u5316\u7406\u89e3\u3002 \u975e\u5e38\u597d\u7684 GDB\uff0cValgrind \u4e0a\u624b\u8bad\u7ec3\uff0c\u4f5c\u4e1a\u4e5f\u4f1a\u6d89\u53ca\u4e00\u4e9b\u57fa\u672c\u7684 Git \u7ec3\u4e60\u3002 \u8001\u5e08\u5efa\u8bae\u4f5c\u4e1a\u7528 Emacs\uff0c\u6240\u4ee5\u5bf9 Emacs \u5c0f\u767d\u6765\u8bf4\uff0c\u662f\u4e2a\u4e0d\u9519\u7684\u5165\u95e8\u3002\u5982\u679c\u4f60\u4f1a\u7528 Vim \uff0c\u6211\u5efa\u8bae\u4f60\u7528 Evil \u63d2\u4ef6\u3002\u8fd9\u6837\u4f60\u4e0d\u4f1a\u4e22\u6389 Vim \u7684\u7f16\u8f91\u529f\u80fd\uff0c\u540c\u65f6\u53ef\u4ee5\u4f53\u4f1a Emacs \u7684\u5f3a\u5927\u3002\u5de5\u5177\u7bb1\u91cc\u540c\u65f6\u6709 Emacs \u548c Vim \u65f6\uff0c\u6548\u7387\u4f1a\u6709\u4e0d\u5c11\u63d0\u9ad8\u3002Emacs \u7684 org-mode\uff0c\u548c GDB \u7684\u987a\u6ed1\u6574\u5408\uff0c\u7b49\u7b49\u7b49\u7b49\uff0c\u90fd\u4f1a\u8ba9\u4f60\u5982\u864e\u6dfb\u7ffc\u3002 \u867d\u7136\u53ef\u80fd\u9700\u8981\u4ed8\u8d39\uff0c\u4f46\u6211\u89c9\u5f97\u503c\u3002 Coursera \u628a\u8fd9\u4e00\u5927\u8bfe\u5206\u6210\u4e86\u56db\u4e2a\u5c0f\u8bfe\uff0c\u4f46\u5c0f\u8bfe\u4e4b\u95f4\u7684\u4f5c\u4e1a\u6587\u4ef6\u7684\u8f6c\u79fb\u5e76\u4e0d\u987a\u5229\u3002\u8fd9\u662f\u4e2a\u6709\u70b9\u8ba8\u538c\u7684\u5730\u65b9\u3002\u5e78\u8fd0\u7684\u662f\uff0c\u540e\u9762\u7684\u5c0f\u8bfe\u53ef\u4ee5\u7ed9\u4ee5\u524d\u5c0f\u8bfe\u7684\u4f5c\u4e1a\u6253\u5206\uff0c\u6240\u4ee5\u6211\u662f\u63a5\u7740\u628a\u524d\u9762\u7684\u4f5c\u4e1a\u53c8\u505a\u4e86\u4e00\u904d\u3002\u6743\u5f53\u5de9\u56fa\uff0c\u6548\u679c\u4e0d\u9519\u3002 \u867d\u8bf4\u8bfe\u540d\u662f\u5165\u95e8\uff0c\u4f46\u517c\u5177\u5e7f\u5ea6\u548c\u6df1\u5ea6\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/Duke-Coursera-Intro-C/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a https://www.coursera.org/specializations/c-programming \u8bfe\u7a0b\u89c6\u9891\uff1a\u540c\u4e0a \u8bfe\u7a0b\u6559\u6750\uff1a\u540c\u4e0a \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u540c\u4e0a","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/Duke-Coursera-Intro-C/#_3","text":"@haidongji \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7684\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 Duke Coursera Intro C \u4e2d\u3002\u56e0\u4e3a\u65f6\u95f4\u5173\u7cfb\uff0c\u6211\u6700\u540e\u4e00\u8bfe\u6700\u540e\u4e00\u5468\u7684\u51e0\u4e2a\u4f5c\u4e1a\u5230\u76ee\u524d\u8fd8\u6ca1\u6709\u5b8c\u6210\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/MIT-Missing-Semester/","text":"MIT: The Missing Semester of Your CS Education Descriptions Offered by: MIT Prerequisites: None Programming Languages: Shell Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 10 hours Just as the course name indicated, this course will teach the missing things in the university courses. It will cover shell programming, git, vim editor, tmux, ssh, sed, awk and even how to beautify your terminal. Trust me, this will be your first step to become a hacker! Resources Homepage: https://missing.csail.mit.edu/ Records: https://www.youtube.com/playlist?list=PLyzOVJj3bHQuloKGG59rS43e29ro7I57J Assignments: Some exercises after each lecture.","title":"MIT-Missing-Semester"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/MIT-Missing-Semester/#mit-the-missing-semester-of-your-cs-education","text":"","title":"MIT: The Missing Semester of Your CS Education"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/MIT-Missing-Semester/#descriptions","text":"Offered by: MIT Prerequisites: None Programming Languages: Shell Difficulty: \ud83c\udf1f\ud83c\udf1f Class Hour: 10 hours Just as the course name indicated, this course will teach the missing things in the university courses. It will cover shell programming, git, vim editor, tmux, ssh, sed, awk and even how to beautify your terminal. Trust me, this will be your first step to become a hacker!","title":"Descriptions"},{"location":"en/%E7%BC%96%E7%A8%8B%E5%85%A5%E9%97%A8/MIT-Missing-Semester/#resources","text":"Homepage: https://missing.csail.mit.edu/ Records: https://www.youtube.com/playlist?list=PLyzOVJj3bHQuloKGG59rS43e29ro7I57J Assignments: Some exercises after each lecture.","title":"Resources"},{"location":"en/%E7%BC%96%E8%AF%91%E5%8E%9F%E7%90%86/6035/","text":"","title":"6035"},{"location":"en/%E7%BC%96%E8%AF%91%E5%8E%9F%E7%90%86/CS143/","text":"Stanford CS143: Compilers \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aStanford \u5148\u4fee\u8981\u6c42\uff1a\u8ba1\u7b97\u673a\u4f53\u7cfb\u7ed3\u6784 \u7f16\u7a0b\u8bed\u8a00\uff1aJava \u6216 C++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a150 \u5c0f\u65f6 \u65af\u5766\u798f\u7684\u7f16\u8bd1\u539f\u7406\u8bfe\u7a0b\uff0c\u8bbe\u8ba1\u8005\u5f00\u53d1\u4e86\u4e00\u4e2a 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\u7406\u8bba\u90e8\u5206\u57fa\u672c\u6309\u7167\u9f99\u4e66\u7684\u987a\u5e8f\u8986\u76d6\u4e86\u8bcd\u6cd5\u5206\u6790\u3001\u8bed\u6cd5\u5206\u6790\u3001\u8bed\u4e49\u5206\u6790\u3001\u8fd0\u884c\u65f6\u73af\u5883\u3001\u5bc4\u5b58\u5668\u5206\u914d\u3001\u4ee3\u7801\u4f18\u5316\u4e0e\u751f\u6210\u7b49\u5185\u5bb9\uff0c\u5b9e\u8df5\u90e8\u5206\u5219\u76f8\u5e94\u5730\u5206\u4e3a\u8bcd\u6cd5\u5206\u6790\u3001\u8bed\u6cd5\u5206\u6790\u3001\u8bed\u4e49\u5206\u6790\u3001\u4ee3\u7801\u751f\u6210\u56db\u4e2a\u9636\u6bb5\uff0c\u96be\u5ea6\u5faa\u5e8f\u6e10\u8fdb\uff0c\u5e76\u5728\u4f18\u5316\u90e8\u5206\u7ed9\u5b66\u751f\u7559\u4e0b\u4e86\u5f88\u5927\u7684\u8bbe\u8ba1\u7a7a\u95f4\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://web.stanford.edu/class/cs143/ \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV17K4y147Bz \u8bfe\u7a0b\u6559\u6750\uff1a\u9f99\u4e66 \u8bfe\u7a0b\u4f5c\u4e1a\uff1a5 \u4e2a\u4e66\u9762\u4f5c\u4e1a + 5 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\u672c\u8bfe\u7a0b\u5c06\u4f5c\u4e3a\u57fa\u4e8e\u7269\u7406\u7684\u8ba1\u7b97\u673a\u52a8\u753b\u6280\u672f\u5165\u95e8\uff0c\u7740\u91cd\u4ecb\u7ecd\u5404\u79cd\u57fa\u7840\u7684\u7269\u7406\u52a8\u753b\u6a21\u62df\u6280\u672f\u3002 \u8be5\u8bfe\u7a0b\u4e3b\u8981\u6db5\u76d6\u56db\u4e2a\u65b9\u5411\uff0c\u5206\u522b\u4e3a\uff1a1\uff09\u521a\u4f53\u6a21\u62df\uff1b2\uff09\u8d28\u70b9\u5f39\u7c27\u3001\u7ea6\u675f\u4e0e\u5e03\u6599\u6a21\u62df\uff1b3\uff09\u57fa\u4e8e\u6709\u9650\u5143\u7684\u5f39\u6027\u4f53\u6a21\u62df\uff1b4\uff09\u6d41\u4f53\u6a21\u62df\u3002 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\u4e3b\u8981\u4ee5\u6e32\u67d3\u4e3a\u4e3b\uff0c\u800c\u5bf9\u4e8e\u7269\u7406\u6a21\u62df\uff0cGAMES103 \u5219\u662f\u5f88\u68d2\u7684\u5b66\u4e60\u8d44\u6e90\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E5%9B%BE%E5%BD%A2%E5%AD%A6/GAMES103/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a GAMES103 \u8bfe\u7a0b\u89c6\u9891\uff1a bilibili \u53c2\u8003\u8d44\u6599\uff1a \u8bfe\u7a0bPPT \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u5171\u56db\u6b21\u4f5c\u4e1a\uff0c\u53ef\u89c1\u5b98\u65b9BBS\u5c0f\u7a0b\u5e8f\u6216\u975e\u5b98\u65b9\u7684Repo\uff1a GAMES103 HW","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E5%9B%BE%E5%BD%A2%E5%AD%A6/GAMES103/#_3","text":"@indevn \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u8981\u6c42\u90fd\u6c47\u603b\u5728 GAMES103 Unoffical \u4e2d\u3002\u5bf9\u4e8e\u4f5c\u4e1a\u7684\u5177\u4f53\u5b9e\u73b0\uff0c\u5728\u77e5\u4e4e\u4e0a\u6709\u5f88\u591a\u76f8\u5173\u6587\u7ae0\u8fdb\u884c\u4e86\u7ec6\u81f4\u8bb2\u89e3\u53ef\u4ee5\u53c2\u8003\u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E5%9B%BE%E5%BD%A2%E5%AD%A6/GAMES202/","text":"GAMES202 \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUCSB \u5148\u4fee\u8981\u6c42\uff1a\u7ebf\u6027\u4ee3\u6570\uff0c\u9ad8\u7b49\u6570\u5b66\uff0cC++\uff0cGAMES101 \u7f16\u7a0b\u8bed\u8a00\uff1aC++ \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a60 \u5c0f\u65f6 \u5b98\u65b9\u4ecb\u7ecd: \u672c\u8bfe\u7a0b\u5c06\u5168\u9762\u5730\u4ecb\u7ecd\u73b0\u4ee3\u5b9e\u65f6\u6e32\u67d3\u4e2d\u7684\u5173\u952e\u95ee\u9898\u4e0e\u89e3\u51b3\u65b9\u6cd5\u3002\u7531\u4e8e\u5b9e\u65f6\u6e32\u67d3 (>30 FPS) 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\u672c\u8bfe\u7a0b\u5c06\u4ee5\u4e13\u9898\u7684\u5f62\u5f0f\u5448\u73b0\uff0c\u8bfe\u7a0b\u5185\u5bb9\u4f1a\u8986\u76d6\u5b66\u672f\u754c\u4e0e\u5de5\u4e1a\u754c\u7684\u524d\u6cbf\u5185\u5bb9\uff0c\u5305\u62ec\uff1a\uff081\uff09\u5b9e\u65f6\u8f6f\u9634\u5f71\u7684\u6e32\u67d3\uff1b\uff082\uff09\u73af\u5883\u5149\u7167\uff1b\uff083\uff09\u57fa\u4e8e\u9884\u8ba1\u7b97\u6216\u65e0\u9884\u8ba1\u7b97\u7684\u5168\u5c40\u5149\u7167\uff1b\uff084\uff09\u57fa\u4e8e\u7269\u7406\u7684\u7740\u8272\u6a21\u578b\u4e0e\u65b9\u6cd5\uff1b\uff085\uff09\u5b9e\u65f6\u5149\u7ebf\u8ffd\u8e2a\uff1b\uff086\uff09\u6297\u952f\u9f7f\u4e0e\u8d85\u91c7\u6837\uff1b\u4ee5\u53ca\u4e00\u4e9b\u5e38\u89c1\u7684\u52a0\u901f\u65b9\u5f0f\u7b49\u7b49\u3002 \u9664\u4e86\u6700\u65b0\u6700\u5168\u7684\u5185\u5bb9\u4e4b\u5916\uff0c\u672c\u8bfe\u7a0b\u4e0e\u5176\u5b83\u4efb\u4f55\u5b9e\u65f6\u6e32\u67d3\u7684\u6559\u7a0b\u8fd8\u6709\u4e00\u4e2a\u91cd\u8981\u7684\u533a\u522b\uff0c\u90a3\u5c31\u662f\u672c\u8bfe\u7a0b\u4e0d\u4f1a\u8bb2\u6388\u4efb\u4f55\u4e0e\u6e38\u620f\u5f15\u64ce\u7684\u4f7f\u7528\u76f8\u5173\u7684\u5185\u5bb9\uff0c\u5e76\u4e14\u4e0d\u4f1a\u7279\u522b\u5f3a\u8c03\u5177\u4f53\u7684\u7740\u8272\u5668\u5b9e\u73b0\u6280\u672f\uff0c\u800c\u4e3b\u8981\u8bb2\u6388\u5b9e\u65f6\u6e32\u67d3\u80cc\u540e\u7684\u79d1\u5b66\u4e0e\u77e5\u8bc6\u3002\u672c\u8bfe\u7a0b\u7684\u76ee\u6807\u662f\u5728\u4f60\u5b66\u4e60\u5b8c\u8fd9\u95e8\u8bfe\u7684\u65f6\u5019\uff0c\u4f60\u5c06\u6709\u6df1\u539a\u7684\u529f\u5e95\u53bb\u5f00\u53d1\u4e00\u4e2a\u5c5e\u4e8e\u4f60\u81ea\u5df1\u7684\u5b9e\u65f6\u6e32\u67d3\u5f15\u64ce\u3002 \u4f5c\u4e3a GAMES101 \u7684\u8fdb\u9636\u8bfe\u7a0b\uff0c\u96be\u5ea6\u6709\u4e00\u5b9a\u7684\u63d0\u5347\uff0c\u4f46\u4e0d\u4f1a\u5f88\u5927\uff0c\u76f8\u4fe1\u5b8c\u6210\u4e86 GAMES101 \u7684\u540c\u5b66\u90fd\u6709\u80fd\u529b\u5b8c\u6210\u8fd9\u95e8\u8bfe\u7a0b\u3002\u6bcf\u4e2a project \u4ee3\u7801\u91cf\u90fd\u4e0d\u4f1a\u5f88\u591a\uff0c\u4f46\u662f\u90fd\u9700\u8981\u4e00\u5b9a\u7684\u601d\u8003\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a GAMES202 \u8bfe\u7a0b\u89c6\u9891\uff1a bilibili \u8bfe\u7a0b\u6559\u6750\uff1aReal-Time Rendering, 4th edition. \u8bfe\u7a0b\u4f5c\u4e1a\uff1a 5\u4e2aproject","title":"GAMES202"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E5%9B%BE%E5%BD%A2%E5%AD%A6/GAMES202/#games202","text":"","title":"GAMES202"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E5%9B%BE%E5%BD%A2%E5%AD%A6/GAMES202/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aUCSB \u5148\u4fee\u8981\u6c42\uff1a\u7ebf\u6027\u4ee3\u6570\uff0c\u9ad8\u7b49\u6570\u5b66\uff0cC++\uff0cGAMES101 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\u7684\u8fdb\u9636\u8bfe\u7a0b\uff0c\u96be\u5ea6\u6709\u4e00\u5b9a\u7684\u63d0\u5347\uff0c\u4f46\u4e0d\u4f1a\u5f88\u5927\uff0c\u76f8\u4fe1\u5b8c\u6210\u4e86 GAMES101 \u7684\u540c\u5b66\u90fd\u6709\u80fd\u529b\u5b8c\u6210\u8fd9\u95e8\u8bfe\u7a0b\u3002\u6bcf\u4e2a project \u4ee3\u7801\u91cf\u90fd\u4e0d\u4f1a\u5f88\u591a\uff0c\u4f46\u662f\u90fd\u9700\u8981\u4e00\u5b9a\u7684\u601d\u8003\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E5%9B%BE%E5%BD%A2%E5%AD%A6/GAMES202/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a GAMES202 \u8bfe\u7a0b\u89c6\u9891\uff1a bilibili \u8bfe\u7a0b\u6559\u6750\uff1aReal-Time Rendering, 4th edition. \u8bfe\u7a0b\u4f5c\u4e1a\uff1a 5\u4e2aproject","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/CS144/","text":"CS144: Computer Network Introduction Offered by: Stanford Prerequisites: Computer System Fundamentals, CS106L Programming Language: C++ Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours One of the lecturers of this course is Professor Nick McKeown , a giant in the field of Networking. At the end of each chapter of MOOC, he will interview an executive in the industry or an expert in the academia, which can certainly broaden your horizons. In the projects, you will use C++ to build the entire TCP/IP protocol stack, the IP router, and the ARP protocol step by step from scratch. Finally, you will replace Linux Kernel's protocol stack with your own and use socket programming to communicate with your classmates, which is really amazing and exciting. Resources Course Website: https://cs144.github.io/ Video: https://www.youtube.com/watch?v=r2WZNaFyrbQ&list=PL6RdenZrxrw9inR-IJv-erlOKRHjymxMN Textbook: None Assignments: refer to the course website Reference PKUFlyingPig Lexssama's Blogs huangrt01 kiprey \u5eb7\u5b87PL's Blog doraemonzzz ViXbob's libsponge \u5403\u7740\u571f\u8c46\u5750\u5730\u94c1\u7684\u535a\u5ba2 Smith \u661f\u9065\u89c1 EIMadrigal Joey","title":"Stanford CS144: Computer Network"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/CS144/#cs144-computer-network","text":"","title":"CS144: Computer Network"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/CS144/#introduction","text":"Offered by: Stanford Prerequisites: Computer System Fundamentals, CS106L Programming Language: C++ Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 100 hours One of the lecturers of this course is Professor Nick McKeown , a giant in the field of Networking. At the end of each chapter of MOOC, he will interview an executive in the industry or an expert in the academia, which can certainly broaden your horizons. In the projects, you will use C++ to build the entire TCP/IP protocol stack, the IP router, and the ARP protocol step by step from scratch. Finally, you will replace Linux Kernel's protocol stack with your own and use socket programming to communicate with your classmates, which is really amazing and exciting.","title":"Introduction"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/CS144/#resources","text":"Course Website: https://cs144.github.io/ Video: https://www.youtube.com/watch?v=r2WZNaFyrbQ&list=PL6RdenZrxrw9inR-IJv-erlOKRHjymxMN Textbook: None Assignments: refer to the course website","title":"Resources"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/CS144/#reference","text":"PKUFlyingPig Lexssama's Blogs huangrt01 kiprey \u5eb7\u5b87PL's Blog doraemonzzz ViXbob's libsponge \u5403\u7740\u571f\u8c46\u5750\u5730\u94c1\u7684\u535a\u5ba2 Smith \u661f\u9065\u89c1 EIMadrigal Joey","title":"Reference"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/topdown/","text":"Computer Networking: A Top-Down Approach Descriptions Offered by: UMass Prerequisites: basic knowledge about computer system Programming Languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 40 hours Computer Networking: A Top-Down Approach is a classic textbook in the field of computer networking. The two authors, Jim Kurose and Keith Ross, have carefully crafted a course website to support the textbook, with lecture recordings, interactive online questions, and WireShark labs for network packet analysis. The only pity is that this course doesn't have hardcore programming assignments, and Stanford's CS144 makes up for that. Course Resources Course Website: https://gaia.cs.umass.edu/kurose_ross/index.php Recordings: https://gaia.cs.umass.edu/kurose_ross/lectures.php Textbooks: Computer Networking: A Top-Down Approach Assignments: https://gaia.cs.umass.edu/kurose_ross/wireshark.php Personal Resources All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/Computer-Network-A-Top-Down-Approach - GitHub .","title":"Computer Networking: A Top-Down Approach"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/topdown/#computer-networking-a-top-down-approach","text":"","title":"Computer Networking: A Top-Down Approach"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/topdown/#descriptions","text":"Offered by: UMass Prerequisites: basic knowledge about computer system Programming Languages: None Difficulty: \ud83c\udf1f\ud83c\udf1f\ud83c\udf1f Class Hour: 40 hours Computer Networking: A Top-Down Approach is a classic textbook in the field of computer networking. The two authors, Jim Kurose and Keith Ross, have carefully crafted a course website to support the textbook, with lecture recordings, interactive online questions, and WireShark labs for network packet analysis. The only pity is that this course doesn't have hardcore programming assignments, and Stanford's CS144 makes up for that.","title":"Descriptions"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/topdown/#course-resources","text":"Course Website: https://gaia.cs.umass.edu/kurose_ross/index.php Recordings: https://gaia.cs.umass.edu/kurose_ross/lectures.php Textbooks: Computer Networking: A Top-Down Approach Assignments: https://gaia.cs.umass.edu/kurose_ross/wireshark.php","title":"Course Resources"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/topdown/#personal-resources","text":"All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/Computer-Network-A-Top-Down-Approach - GitHub .","title":"Personal Resources"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/topdown_ustc/","text":"USTC Computer Networking:A Top-Down Approach \u8bfe\u7a0b\u7b80\u4ecb 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\u8bfe\u7a0b\u89c6\u9891\u662f\u90d1\u70c7\u8001\u5e08\u672c\u4eba\u5728\u54d4\u54e9\u54d4\u54e9\u4e0a\u4e0a\u4f20\u7684\uff0c\u4e14\u4ece2020\u5e74\u81f3\u4eca\u4e00\u76f4\u575a\u6301\u5728\u8bc4\u8bba\u533a\u7b54\u7591\uff0c\u5c5e\u5b9e\u8d1f\u8d23\u4e14\u8ba4\u771f\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C/topdown_ustc/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a http://staff.ustc.edu.cn/~qzheng/teaching.html \u8bfe\u7a0b\u89c6\u9891\uff1a https://www.bilibili.com/video/BV1JV411t7ow/ \u8bfe\u7a0b\u8bfe\u4ef6\uff1a http://staff.ustc.edu.cn/~qzheng/cn.zip \u8bfe\u7a0b\u6559\u6750\uff1a\u8ba1\u7b97\u673a\u7f51\u7edc\uff08\u81ea\u9876\u5411\u4e0b\u65b9\u6cd5 \u7b2c7\u7248\uff09\uff0c\u673a\u68b0\u5de5\u4e1a\u51fa\u7248\u793e\uff0c2016","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/6031/","text":"MIT 6.031: Software Construction \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u638c\u63e1\u81f3\u5c11\u4e00\u95e8\u7f16\u7a0b\u8bed\u8a00 \u7f16\u7a0b\u8bed\u8a00\uff1aJava \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u7684\u76ee\u6807\u5c31\u662f\u8ba9\u5b66\u751f\u5b66\u4f1a\u5982\u4f55\u5199\u51fa\u9ad8\u8d28\u91cf\u7684\u4ee3\u7801\uff0c\u6240\u8c13\u9ad8\u8d28\u91cf\uff0c\u5219\u662f\u6ee1\u8db3\u4e0b\u9762\u4e09\u4e2a\u76ee\u6807\uff08\u8bfe\u7a0b\u8bbe\u8ba1\u8005\u539f\u8bdd\u590d\u5236\uff0c\u4ee5\u9632\u81ea\u5df1\u7ffb\u8bd1\u66f2\u89e3\u672c\u610f\uff09\uff1a Safe from bugs. Correctness (correct behavior right now) and defensiveness (correct behavior in the future) are required in any software we build. Easy to understand. The code has to communicate to future programmers who need to understand it and make changes in it (fixing bugs or adding new features). That future programmer might be you, months or years from now. You\u2019ll be surprised how much you forget if you don\u2019t write it down, and how much it helps your own future self to have a good design. Ready for change. Software always changes. Some designs make it easy to make changes; others require throwing away and rewriting a lot of code. \u4e3a\u6b64\uff0c\u8fd9\u95e8\u8bfe\u7684\u8bbe\u8ba1\u8005\u4eec\u7cbe\u5fc3\u7f16\u5199\u4e86\u4e00\u672c\u4e66\u6765\u9610\u91ca\u8bf8\u591a\u8f6f\u4ef6\u6784\u5efa\u7684\u6838\u5fc3\u539f\u5219\u4e0e\u524d\u4eba\u603b\u7ed3\u4e0b\u6765\u7684\u5b9d\u8d35\u7ecf\u9a8c\uff0c\u5185\u5bb9\u7ec6\u8282\u5230\u5982\u4f55\u7f16\u5199\u6ce8\u91ca\u548c\u51fd\u6570 Specification\uff0c\u5982\u4f55\u8bbe\u8ba1\u62bd\u8c61\u6570\u636e\u7ed3\u6784\u4ee5\u53ca\u8bf8\u591a\u5e76\u884c\u7f16\u7a0b\u7684\u5185\u5bb9\uff0c\u5e76\u4e14\u4f1a\u8ba9\u4f60\u5728\u7cbe\u5fc3\u8bbe\u8ba1\u7684 Java \u7f16\u7a0b\u9879\u76ee\u91cc\u4f53\u9a8c\u548c\u7ec3\u4e60\u8fd9\u4e9b\u7f16\u7a0b\u6a21\u5f0f\u3002 2016\u5e74\u6625\u5b63\u5b66\u671f\u8fd9\u95e8\u8bfe\u5f00\u6e90\u4e86\u5176\u6240\u6709\u7f16\u7a0b\u4f5c\u4e1a\u7684\u4ee3\u7801\u6846\u67b6\uff0c\u800c\u6700\u65b0\u7684\u8bfe\u7a0b\u6559\u6750\u53ef\u4ee5\u5728\u5176\u6700\u65b0\u7684\u6559\u5b66\u7f51\u7ad9\u4e0a\u627e\u5230\uff0c\u5177\u4f53\u94fe\u63a5\u53c2\u89c1\u4e0b\u65b9\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a 2021spring , 2016spring \u8bfe\u7a0b\u89c6\u9891\uff1a\u65e0 \u8bfe\u7a0b\u6559\u6750\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9\u7684\u8bfe\u7a0b notes \u8bfe\u7a0b\u4f5c\u4e1a\uff1a4 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a + 1 \u4e2a Project \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/MIT6.031-software-construction - GitHub \u4e2d\u3002 @pengzhangzhi \u5b8c\u6210\u4e86\u8fd9\u95e8\u8bfe\u7684\u4f5c\u4e1a\u5e76\u8bb0\u5f55\u4e86\u7b14\u8bb0, \u4ee3\u7801\u5f00\u6e90\u5728 pengzhangzhi/self-taught-CS/Software Construction - Github \u3002","title":"MIT 6.031: Software Construction"},{"location":"en/%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/6031/#mit-6031-software-construction","text":"","title":"MIT 6.031: Software Construction"},{"location":"en/%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/6031/#_1","text":"\u6240\u5c5e\u5927\u5b66\uff1aMIT \u5148\u4fee\u8981\u6c42\uff1a\u638c\u63e1\u81f3\u5c11\u4e00\u95e8\u7f16\u7a0b\u8bed\u8a00 \u7f16\u7a0b\u8bed\u8a00\uff1aJava \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u8fd9\u95e8\u8bfe\u7684\u76ee\u6807\u5c31\u662f\u8ba9\u5b66\u751f\u5b66\u4f1a\u5982\u4f55\u5199\u51fa\u9ad8\u8d28\u91cf\u7684\u4ee3\u7801\uff0c\u6240\u8c13\u9ad8\u8d28\u91cf\uff0c\u5219\u662f\u6ee1\u8db3\u4e0b\u9762\u4e09\u4e2a\u76ee\u6807\uff08\u8bfe\u7a0b\u8bbe\u8ba1\u8005\u539f\u8bdd\u590d\u5236\uff0c\u4ee5\u9632\u81ea\u5df1\u7ffb\u8bd1\u66f2\u89e3\u672c\u610f\uff09\uff1a Safe from bugs. Correctness (correct behavior right now) and defensiveness (correct behavior in the future) are required in any software we build. Easy to understand. The code has to communicate to future programmers who need to understand it and make changes in it (fixing bugs or adding new features). That future programmer might be you, months or years from now. You\u2019ll be surprised how much you forget if you don\u2019t write it down, and how much it helps your own future self to have a good design. Ready for change. Software always changes. Some designs make it easy to make changes; others require throwing away and rewriting a lot of code. \u4e3a\u6b64\uff0c\u8fd9\u95e8\u8bfe\u7684\u8bbe\u8ba1\u8005\u4eec\u7cbe\u5fc3\u7f16\u5199\u4e86\u4e00\u672c\u4e66\u6765\u9610\u91ca\u8bf8\u591a\u8f6f\u4ef6\u6784\u5efa\u7684\u6838\u5fc3\u539f\u5219\u4e0e\u524d\u4eba\u603b\u7ed3\u4e0b\u6765\u7684\u5b9d\u8d35\u7ecf\u9a8c\uff0c\u5185\u5bb9\u7ec6\u8282\u5230\u5982\u4f55\u7f16\u5199\u6ce8\u91ca\u548c\u51fd\u6570 Specification\uff0c\u5982\u4f55\u8bbe\u8ba1\u62bd\u8c61\u6570\u636e\u7ed3\u6784\u4ee5\u53ca\u8bf8\u591a\u5e76\u884c\u7f16\u7a0b\u7684\u5185\u5bb9\uff0c\u5e76\u4e14\u4f1a\u8ba9\u4f60\u5728\u7cbe\u5fc3\u8bbe\u8ba1\u7684 Java \u7f16\u7a0b\u9879\u76ee\u91cc\u4f53\u9a8c\u548c\u7ec3\u4e60\u8fd9\u4e9b\u7f16\u7a0b\u6a21\u5f0f\u3002 2016\u5e74\u6625\u5b63\u5b66\u671f\u8fd9\u95e8\u8bfe\u5f00\u6e90\u4e86\u5176\u6240\u6709\u7f16\u7a0b\u4f5c\u4e1a\u7684\u4ee3\u7801\u6846\u67b6\uff0c\u800c\u6700\u65b0\u7684\u8bfe\u7a0b\u6559\u6750\u53ef\u4ee5\u5728\u5176\u6700\u65b0\u7684\u6559\u5b66\u7f51\u7ad9\u4e0a\u627e\u5230\uff0c\u5177\u4f53\u94fe\u63a5\u53c2\u89c1\u4e0b\u65b9\u3002","title":"\u8bfe\u7a0b\u7b80\u4ecb"},{"location":"en/%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/6031/#_2","text":"\u8bfe\u7a0b\u7f51\u7ad9\uff1a 2021spring , 2016spring \u8bfe\u7a0b\u89c6\u9891\uff1a\u65e0 \u8bfe\u7a0b\u6559\u6750\uff1a\u53c2\u89c1\u8bfe\u7a0b\u7f51\u7ad9\u7684\u8bfe\u7a0b notes \u8bfe\u7a0b\u4f5c\u4e1a\uff1a4 \u4e2a\u7f16\u7a0b\u4f5c\u4e1a + 1 \u4e2a Project","title":"\u8bfe\u7a0b\u8d44\u6e90"},{"location":"en/%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/6031/#_3","text":"@PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/MIT6.031-software-construction - GitHub \u4e2d\u3002 @pengzhangzhi \u5b8c\u6210\u4e86\u8fd9\u95e8\u8bfe\u7684\u4f5c\u4e1a\u5e76\u8bb0\u5f55\u4e86\u7b14\u8bb0, \u4ee3\u7801\u5f00\u6e90\u5728 pengzhangzhi/self-taught-CS/Software Construction - Github \u3002","title":"\u8d44\u6e90\u6c47\u603b"},{"location":"en/%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B/CS169/","text":"UCB CS169: software engineering \u8bfe\u7a0b\u7b80\u4ecb \u6240\u5c5e\u5927\u5b66\uff1aUC Berkeley \u5148\u4fee\u8981\u6c42\uff1a\u65e0 \u7f16\u7a0b\u8bed\u8a00\uff1aRuby/JavaScript \u8bfe\u7a0b\u96be\u5ea6\uff1a\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f\ud83c\udf1f \u9884\u8ba1\u5b66\u65f6\uff1a100 \u5c0f\u65f6 \u4f2f\u514b\u5229\u7684\u8f6f\u4ef6\u5de5\u7a0b\u8bfe\u7a0b\uff0c\u4e0d\u540c\u4e8e\u5f88\u591a\u4f20\u7edf\u7684\u8f6f\u4ef6\u5de5\u7a0b\u8bfe\u5f3a\u8c03\u5404\u79cd\u7c7b\u56fe\u3001\u6587\u6863\u8bbe\u8ba1 (plan and document \u6a21\u5f0f)\uff0c\u8fd9\u95e8\u8bfe\u4e13\u6ce8\u4e8e\u6700\u8fd1\u9010\u6e10\u6d41\u884c\u8d77\u6765\u7684\u654f\u6377\u5f00\u53d1 (Agile Development)\u6a21\u5f0f\uff0c\u5229\u7528\u4e91\u5e73\u53f0\u63d0\u4f9b\u8f6f\u4ef6\u5373\u670d\u52a1 (software as a service)\u3002\u4e3a\u6b64\uff0c\u8bfe\u7a0b\u8bbe\u8ba1\u8005\u7f16\u5199\u4e86 Software as a service \u8fd9\u672c\u6559\u6750\uff0c\u901a\u8fc7 Ruby/Rails \u6846\u67b6\u6765\u9610\u91ca SaaS \u8fd9\u4e2a\u6982\u5ff5\uff0c\u5e76\u4e14\u6709\u4e30\u5bcc\u7684\u914d\u5957\u7f16\u7a0b\u7ec3\u4e60\u3002 \u8fd9\u95e8\u8bfe\u5728 Edx \u8fd9\u4e2a\u7531 MIT \u548c Havard \u5927\u5b66\u53d1\u8d77\u7684\u5728\u7ebf\u6559\u80b2\u5e73\u53f0\u5168\u8d44\u6599\u5f00\u6e90\uff0c\u5927\u5bb6\u53ef\u4ee5\u5728 Edx \u81ea\u884c\u641c\u7d22 Agile SaaS Development \u8fd9\u95e8\u8bfe\u7a0b\u8fdb\u884c\u5b66\u4e60\u3002\u8bfe\u7a0b\u5185\u5bb9\u57fa\u672c\u6309\u7167\u6559\u6750\u7684\u987a\u5e8f\u5e26\u4f60\u4e00\u6b65\u6b65\u4ee5\u654f\u6377\u5f00\u53d1\u7684\u65b9\u5f0f\u642d\u5efa\u4e00\u4e2a\u8f6f\u4ef6\u5e76\u514d\u8d39\u90e8\u7f72\u5728\u4e91\u5e73\u53f0\u4e0a\u3002 \u8bfe\u7a0b\u8d44\u6e90 \u8bfe\u7a0b\u7f51\u7ad9\uff1a http://www.saasbook.info/courses \u8bfe\u7a0b\u89c6\u9891\uff1a\u53c2\u89c1 Edx \u8bfe\u7a0b\u4e3b\u9875\u3002 \u8bfe\u7a0b\u6559\u6750\uff1a Software as a service \u8bfe\u7a0b\u4f5c\u4e1a\uff1a\u53c2\u89c1 Edx \u8bfe\u7a0b\u4e3b\u9875\u3002 \u8d44\u6e90\u6c47\u603b @PKUFlyingPig \u5728\u5b66\u4e60\u8fd9\u95e8\u8bfe\u4e2d\u7528\u5230\u7684\u6240\u6709\u8d44\u6e90\u548c\u4f5c\u4e1a\u5b9e\u73b0\u90fd\u6c47\u603b\u5728 PKUFlyingPig/CS169-Software-Engineering - 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