add nav

* Update Git.md

* Update Git.en.md

* Update Git.en.md

* Update Git.md

README.md

@@ -6,10 +6,14 @@
 
 > *Everyone should enjoy CS if you have a good teacher to teach you a good course.*
 
+<a href="https://trendshift.io/repositories/4643" target="_blank"><img src="https://trendshift.io/api/badge/repositories/4643" alt="PKUFlyingPig%2Fcs-self-learning | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
+
+[![Website](https://img.shields.io/badge/website-csdiy.wiki-blue)](https://csdiy.wiki)
 [![License](https://img.shields.io/github/license/PKUFlyingPig/cs-self-learning)](https://github.com/PKUFlyingPig/cs-self-learning/blob/master/LICENSE)
 [![Issues](https://img.shields.io/github/issues/PKUFlyingPig/cs-self-learning)](https://github.com/PKUFlyingPig/cs-self-learning/issues)
 [![Stars](https://img.shields.io/github/stars/PKUFlyingPig/cs-self-learning)](https://github.com/PKUFlyingPig/cs-self-learning)
 
+
 新冠肆虐，网课当道，CS 爆火，这一系列的事件都让自学计算机成为了一种潮流。
 
 随着欧美众多名校将质量极高的计算机课程全部开源，自学 CS 成了一件可操作性极强的事情。毫不夸张地说，只要你有毅力和兴趣，自学的成果完全不亚于你在国内任何一所大学受到的本科 CS 教育（当然，这里单指计算机专业领域，大学带给你的显然不止是专业知识）。
@@ -22,9 +26,7 @@
 
 我的目标是让一个刚刚接触计算机的小白，可以完全凭借这些开源社区的优质资源，少走弯路，在 2-3 年内成长为一个有扎实的数学功底和代码能力，经历过数十个千行代码量的 Project 的洗礼，掌握至少 C/C++/Java/JS/Python/Go/Rust 等主流语言，对算法、电路、体系、网络、操统、编译、人工智能、机器学习、计算机视觉、自然语言处理、强化学习、密码学、信息论、博弈论、数值分析、统计学、分布式、数据库、图形学、Web 开发、云服务、超算等等方面均有所涉猎的全能程序员。此后，无论是选择科研还是就业，我相信你都会有相当的竞争力。
 
-你可以[在线免费阅读这本书](https://csdiy.wiki)。
-
-英文版请移步[这里](https://github.com/PKUFlyingPig/Self-learning-Computer-Science)。
+你可以[在线免费阅读这本书](https://csdiy.wiki)。英文版请移步[这里](https://csdiy.wiki/en/)。
 
 ## 如何成为贡献者
 
@@ -32,7 +34,7 @@
 
 对于中英混合排版的要点规范，可以参考[这个仓库](https://github.com/sparanoid/chinese-copywriting-guidelines/blob/master/README.zh-Hans.md)，我们将会对您的 Pull Request 做相应的校对，具体原因参见这个 [issue](https://github.com/PKUFlyingPig/cs-self-learning/issues/114)。
 
-本书的英文版也正在翻译中，如果你想参与到翻译的队伍里，可以参考这个 [issue](https://github.com/PKUFlyingPig/cs-self-learning/issues/222)。
+本书支持英文版，因此贡献的内容需要提供对应的英文翻译，具体流程可以参考这个 [issue](https://github.com/PKUFlyingPig/cs-self-learning/issues/222)。
 
 同时由于个人水平有限，书中难免有笔误甚至概念错误之处，也请各位不吝赐教，在 issue 中提出来。
 

docs/CS学习规划.en.md

@@ -0,0 +1,372 @@
+# A Reference Guide for CS Learning
+
+The field of computer science is vast and complex, with a seemingly endless sea of knowledge. Each specialized area can lead to limitless learning if pursued deeply. Therefore, a clear and definite study plan is very important. I've taken some detours in my years of self-study and finally distilled the following content for your reference.
+
+Before you start learning, I highly recommend a popular science video series for beginners: [Crash Course: Computer Science](https://www.bilibili.com/video/BV1EW411u7th). In just 8 hours, it vividly and comprehensively covers various aspects of computer science: the history of computers, how computers operate, the important modules that make up a computer, key ideas in computer science, and so on. As its slogan says, *Computers are not magic!* I hope that after watching this video, everyone will have a holistic perception of computer science and embark on the detailed and in-depth learning content below with interest.
+
+## Essential Tools
+
+> As the saying goes: sharpening your axe will not delay your job of chopping wood. If you are a pure beginner in the world of computers, learning some tools will make you more efficient.
+
+**Learn to ask questions**: You might be surprised that asking questions is the first one listed? I think in the open-source community, learning to ask questions is a very important ability. It involves two aspects. First, it indirectly cultivates your ability to solve problems independently, as the cycle of forming a question, describing it, getting answers from others, and then understanding the response is quite long. If you expect others to remotely assist you with every trivial issue, then the world of computers might not suit you. Second, if after trying, you still can't solve a problem, you can seek help from the open-source community. But at that point, how to concisely explain your situation and goal to others becomes particularly important. I recommend reading the article [How To Ask Questions The Smart Way](https://github.com/ryanhanwu/How-To-Ask-Questions-The-Smart-Way/blob/main/README-zh_CN.md), which not only increases the probability and efficiency of solving your problems but also keeps those who provide answers in the open-source community in a good mood.
+
+**Learn to be a hacker**: [MIT-Missing-Semester](./编程入门/MIT-Missing-Semester.md) covers many useful tools for a hacker and provides detailed usage instructions. I strongly recommend beginners to study this course. However, one thing to note is that the course occasionally refers to terms related to the development process. Therefore, it is recommended to study it at least after completing an introductory computer science course.
+
+**[GFW](./必学工具/翻墙.md)**: For well-known reasons, sites like Google and GitHub are not accessible in mainland China. However, in many cases, Google and StackOverflow can solve 99% of the problems encountered during development. Therefore, learning to use a VPN is almost an essential skill for a mainland CSer. (Considering legal issues, the methods provided in this book are only applicable to users with a Peking University email address).
+
+**Command Line**: Proficiency in using the command line is often overlooked or considered difficult to master, but in reality, it greatly enhances your flexibility and productivity as an engineer. [The Art of Command Line](https://github.com/jlevy/the-art-of-command-line/blob/master/README-zh.md) is a classic tutorial that started as a question on Quora, but with the contribution of many experts, it has become a top GitHub project with over 100,000 stars, translated into dozens of languages. The tutorial is not long, and I highly recommend everyone to read it repeatedly and internalize it through practice. Also, mastering shell script programming should not be overlooked, and you can refer to this [tutorial](https://www.shellscript.sh/).
+
+**IDE (Integrated Development Environment)**: Simply put, it's where you write your code. The importance of an IDE for a programmer goes without saying, but many IDEs are designed for large-scale projects and are quite bulky and overly feature-rich. Nowadays, some lightweight text editors with rich plugin ecosystems can basically meet the needs of daily lightweight programming. My personal favorites are VS Code and Sublime (the former has a very simple plugin configuration, while the latter is a bit more complex but aesthetically pleasing). Of course, for large projects, I would still use slightly heavier IDEs, such as Pycharm (Python), IDEA (Java), etc. (Disclaimer: all IDEs are the best in the world).
+
+**[Vim](./必学工具/Vim.md)**: A command-line editor. Vim has a somewhat steep learning curve, but mastering it, I think, is very necessary because it will greatly improve your development efficiency. Most modern IDEs also support Vim plugins, allowing you to retain the coolness of a geek while enjoying a modern development environment.
+
+**[Emacs](./必学工具/Emacs.md)**: A classic editor that stands alongside Vim, with equally high development efficiency and more powerful expandability. It can be configured as a lightweight editor or expanded into a custom IDE, and even more sophisticated tricks.
+
+**[Git](./必学工具/Git.md)**: A version control tool for your project. Git, created by the father of Linux, Linus, is definitely one of the must-have tools for every CS student.
+
+**[GitHub](./必学工具/GitHub.md)**: A code hosting platform based on Git. The world's largest open-source community and a gathering place for CS experts.
+
+**[GNU Make](./必学工具/GNU_Make.md)**: An engineering build tool. Proficiency in GNU Make will help you develop a habit of modularizing your code and familiarize you with the compilation and linking processes of large projects.
+
+**[CMake](./必学工具/CMake.md)**: A more powerful build tool than GNU Make, recommended for study after mastering GNU Make.
+
+**[LaTex](./必学工具/LaTeX.md)**: <del>Pretentious</del> Paper typesetting tool.
+
+**[Docker](./必学工具/Docker.md)**: A lighter-weight software packaging and deployment tool compared to virtual machines.
+
+**[Practical Toolkit](./必学工具/tools.md)**: In addition to the tools mentioned above that are frequently used in development, I have also collected many practical and interesting free tools, such as download tools, design tools, learning websites, etc.
+
+**[Thesis](./必学工具/thesis.md)**: Tutorial for writing graduation thesis in Word.
+
+## Recommended Books
+
+> I believe a good textbook should be people-oriented, rather than a display of technical jargon. It's certainly important to tell readers "what it is," but a better approach would be for the author to integrate decades of experience in the field into the book and narratively convey to the reader "why it is" and what should be done in the future.
+
+[Link here](./好书推荐.md)
+
+## Environment Setup
+
+> What you think of as development — coding frantically in an IDE for hours.
+>
+> Actual development — setting up the environment for several days without starting to code.
+
+### PC Environment Setup
+
+If you are a Mac user, you're in luck, as this [guide](https://sourabhbajaj.com/mac-setup/) will walk you through setting up the entire development environment. If you are a Windows user, thanks to the efforts of the open-source community, you can enjoy a similar experience with [Scoop](./必学工具/Scoop.md).
+
+Additionally, you can refer to an [environment setup guide][guide] inspired by [6.NULL MIT-Missing-Semester](./编程入门/MIT-Missing-Semester.md), focusing on terminal beautification. It also includes common software sources (such as GitHub, Anaconda, PyPI) for acceleration and replacement, as well as some IDE configuration and activation tutorials.
+
+[guide]: https://taylover2016.github.io/%E6%96%B0%E6%9C%BA%E5%99%A8%E4%B8%8A%E6%89%8B%E6%8C%87%E5%8D%97%EF%BC%88%E6%96%B0%E6%89%8B%E5%90%91%EF%BC%89/index.html
+
+### Server-Side Environment Setup
+
+Server-side operation and maintenance require basic use of Linux (or other Unix-like systems) and fundamental concepts like processes, devices, networks, etc. Beginners can refer to the [Linux 101](https://101.lug.ustc.edu.cn/) online notes compiled by the Linux User Association of the University of Science and Technology of China. If you want to delve deeper into system operation and maintenance, you can refer to the [Aspects of System Administration](https://stevens.netmeister.org/615/) course.
+
+Additionally, if you need to learn a specific concept or tool, I recommend a great GitHub project, [DevOps-Guide](https://github.com/Tikam02/DevOps-Guide), which covers a lot of foundational knowledge and tutorials in the administration field, such as Docker, Kubernetes, Linux, CI-CD, GitHub Actions, and more.
+
+## Course Map
+
+> As mentioned at the beginning of this chapter, this course map is merely a **reference guide** for course planning, from my perspective as an undergraduate nearing graduation. I am acutely aware that I neither have the right nor the capability to preach to others about “how one should learn”. Therefore, if you find any issues with the course categorization and selection below, I fully accept and deeply apologize for them. You can tailor your own course map in the next section [Customize Your Own Course Map](#yourmap).
+
+Apart from courses labeled as *basic* or *introductory*, there is no explicit sequence in the following categories. As long as you meet the prerequisites for a course, you are free to choose any course according to your needs and interests.
+
+### Mathematical Foundations
+
+#### Calculus and Linear Algebra
+
+As a freshman, mastering calculus and linear algebra is as important as learning to code. This point has been reiterated countless times by predecessors, but I feel compelled to emphasize it again: mastering calculus and linear algebra is really important! You might complain that these subjects are forgotten after exams, but I believe that indicates a lack of deep understanding of their essence. If you find the content taught in class to be obscure, consider referring to MIT’s [Calculus Course](./数学基础/MITmaths.md) and [18.06: Linear Algebra](./数学基础/MITLA.md) course notes. For me, they greatly deepened my understanding of the essence of calculus and linear algebra. Also, I highly recommend the maths YouTuber [**3Blue1Brown**](https://www.youtube.com/c/3blue1brown), whose channel features videos explaining the core of mathematics with vivid animations, offering both depth and breadth of high quality.
+
+#### Introduction to Information Theory
+
+For computer science students, gaining some foundational knowledge in information theory early on is beneficial. However, most information theory courses are targeted towards senior or even graduate students, making them quite inaccessible to beginners. MIT’s [6.050J: Information theory and Entropy](./数学基础/information.md) is tailored for freshmen, with almost no prerequisites, covering coding, compression, communication, information entropy, and more, which is very interesting.
+
+### Advanced Mathematics
+
+#### Discrete Mathematics and Probability Theory
+
+Set theory, graph theory, and probability theory are essential tools for algorithm derivation and proof, as well as foundations for more advanced mathematical courses. However, the teaching of these subjects often falls into a rut of being overly theoretical and formalistic, turning classes into mere recitations of theorems and conclusions without helping students grasp the essence of these theories. If theory teaching can be interspersed with examples of algorithm application, students can expand their algorithm knowledge while appreciating the power and charm of theory.
+
+[UCB CS70: Discrete Math and Probability Theory](./数学进阶/CS70.md) and [UCB CS126: Probability Theory](./数学进阶/CS126.md) are UC Berkeley’s probability courses. The former covers the basics of discrete mathematics and probability theory, while the latter delves into stochastic processes and more advanced theoretical content. Both emphasize the integration of theory and practice and feature abundant examples of algorithm application, with the latter including numerous Python programming assignments to apply probability theory to real-world problems.
+
+#### Numerical Analysis
+
+For computer science students, developing computational thinking is crucial. Modeling and discretizing real-world problems, and simulating and analyzing them on computers, are vital skills. Recently, the [Julia](https://julialang.org/) programming language, developed by MIT, has become popular in the field of numerical computation with its C-like speed and Python-friendly syntax. Many MIT mathematics courses have started using Julia as a teaching tool, presenting complex mathematical theories through clear and intuitive code.
+
+[ComputationalThinking](https://computationalthinking.mit.edu/Spring21/) is an introductory course in computational thinking offered by MIT. All course materials are open source and accessible on the course website. Using the Julia programming language, the course covers image processing, social science and data science, and climatology modeling, helping students understand algorithms, mathematical modeling, data analysis, interactive design, and graph presentation. The course content, though not difficult, profoundly impressed me with the idea that the allure of science lies not in obscure theories or jargon but in presenting complex concepts through vivid examples and concise, deep language.
+
+After completing this experience course, if you’re still eager for more, consider MIT’s [18.330: Introduction to Numerical Analysis](./数学进阶/numerical.md). This course also uses Julia for programming assignments but is more challenging and in-depth. It covers floating-point encoding, root finding, linear systems, differential equations, and more, with the main goal of using discrete computer representations to estimate and approximate continuous mathematical concepts. The course instructor has also written an accompanying open-source textbook, [Fundamentals of Numerical Computation](https://fncbook.github.io/fnc/frontmatter.html), which includes abundant Julia code examples and rigorous formula derivations.
+
+If you’re still not satisfied, MIT’s graduate course in numerical analysis, [18.335: Introduction to Numerical Methods][18.335], is also available for reference.
+
+[18.335]: https://ocw.mit.edu/courses/mathematics/18-335j-introduction-to-numerical-methods-spring-2019/index.htm
+
+#### Differential Equations
+
+Wouldn't it be cool if the motion and development of everything in the world could be described and depicted with equations? Although differential equations are not a mandatory part of any CS curriculum, I believe mastering them provides a new perspective to view the world.
+
+Since differential equations often involve complex variable functions, you can refer to [MIT18.04: Complex Variables Functions][MIT18.04] course notes to fill in prerequisite knowledge.
+
+[MIT18.04]: https://ocw.mit.edu/courses/mathematics/18-04-complex-variables-with-applications-spring-2018/
+
+[MIT18.03: Differential Equations][MIT18.03] mainly covers the solution of ordinary differential equations, and on this basis, [MIT18.152: Partial Differential Equations][MIT18.152] dives into the modeling and solving of partial differential equations. With the powerful tool of differential equations, you will gain enhanced capabilities in modeling real-world problems and intuitively grasping the essence among various noisy variables.
+
+[MIT18.03]: https://ocw.mit.edu/courses/mathematics/18-03sc-differential-equations-fall-2011/unit-i-first-order-differential-equations/
+[MIT18.152]: https://ocw.mit.edu/courses/mathematics/18-152-introduction-to-partial-differential-equations-fall-2011/index.htm
+
+### Advanced Mathematical Topics
+
+As a computer science student, I often hear arguments about the uselessness of mathematics. While I neither agree nor have the authority to oppose such views, if everything is forcibly categorized as useful or useless, it indeed becomes quite dull. Therefore, the following advanced mathematics courses, aimed at senior and even graduate students, are available for those interested.
+
+#### Convex Optimization
+
+[Standford EE364A: Convex Optimization](./数学进阶/convex.md)
+
+#### Information Theory
+
+[MIT6.441: Information Theory](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-441-information-theory-spring-2016/syllabus/)
+
+#### Applied Statistics
+
+[MIT18.650: Statistics for Applications](https://ocw.mit.edu/courses/mathematics/18-443-statistics-for-applications-spring-2015/index.htm)
+
+#### Elementary Number Theory
+
+[MIT18.781: Theory of Numbers](https://ocw.mit.edu/courses/mathematics/18-781-theory-of-numbers-spring-2012/index.htm)
+
+#### Cryptography
+
+[Standford CS255: Cryptography](http://crypto.stanford.edu/~dabo/cs255/)
+
+### Programming Fundamentals
+
+> Languages are tools, and you choose the right tool for the right job. Since there's no universally perfect tool, there's no universally perfect language.
+
+#### General
+
+- [MIT-Missing-Semester](编程入门/MIT-Missing-Semester.md)
+- [Harvard CS50: This is CS50x](编程入门/C/CS50.md)
+
+#### Java
+
+- [MIT 6.092: Introduction To Programming In Java](编程入门/Java/MIT%206.092.md)
+
+#### Python
+
+- [CS50P: CS50's Introduction to Programming with Python](编程入门/Python/CS50P.md)
+- [UCB CS61A: Structure and Interpretation of Computer Programs](编程入门/Python/CS61A.md)
+- [MIT6.100L: Introduction to CS and Programming using Python](编程入门/Python/MIT6.100L.md)
+
+#### C++
+
+- [Stanford CS106B/X: Programming Abstractions](编程入门/cpp/CS106B_CS106X.md)
+- [Stanford CS106L: Standard C++ Programming](编程入门/cpp/CS106L.md)
+
+#### Rust
+
+- [Stanford CS110L: Safety in Systems Programming](编程入门/Rust/CS110L.md)
+
+#### OCaml
+
+- [Cornell CS3110: OCaml Programming Correct + Efficient + Beautiful](编程入门/Functional/CS3110.md)
+
+### Electronics Fundamentals
+
+#### Basics of Circuits
+
+For computer science students, understanding basic circuit knowledge and experiencing the entire pipeline from sensor data collection to data analysis and algorithm prediction can be very helpful for future learning and developing computational thinking. [EE16A&B: Designing Information Devices and Systems I&II](./电子基础/EE16.md) at UC Berkeley are introductory courses for freshmen in electrical engineering. EE16A focuses on collecting and analyzing data from the real environment through circuits, while EE16B focuses on analyzing these collected data to make predictive actions.
+
+#### Signals and Systems
+
+Signals and Systems is a course I find very worthwhile. Initially, I studied it out of curiosity about Fourier Transform, but after completing it, I was amazed at how Fourier Transform provided a new perspective to view the world, just like differential equations, immersing you in the elegance and magic of precisely depicting the world with mathematics.
+
+[MIT 6.003: Signal and Systems][MIT6.003] provides all course recordings, written assignments, and answers. You can also check out this course's [ancient version](电子基础/Signals_and_Systems_AVO.md).
+
+[MIT6.003]: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-003-signals-and-systems-fall-2011/lecture-videos/lecture-1-signals-and-systems/
+
+[UCB EE120: Signal and Systems](电子基础/signal.md) has very well-written notes on Fourier Transform and provides many interesting Python programming assignments to practically apply the theories and algorithms of signals and systems.
+
+### Data Structures and Algorithms
+
+Algorithms are the core of computer science and the foundation for almost all professional courses. How to abstract real-world problems into algorithmic problems mathematically and solve them under time and memory constraints using appropriate data structures is the eternal theme of algorithm courses. If you are fed up with your teacher's rote teaching, I highly recommend UC Berkeley's [UCB CS61B: Data Structures and Algorithms](数据结构与算法/CS61B.md) and Princeton's [Coursera: Algorithms I & II](数据结构与算法/Algo.md). Both courses are taught in a deep yet simple manner and have rich and interesting programming experiments to integrate theory with knowledge.
+
+Both of these courses are based on Java. If you prefer C/C++, you can refer to Stanford's data structure and basic algorithm course [Stanford CS106B/X: Programming Abstractions](编程入门/cpp/CS106B_CS106X.md). For those who prefer Python, you can learn MIT's introductory algorithm course [MIT 6.006: Introduction to Algorithms](数据结构与算法/6.006.md).
+
+For those interested in more advanced algorithms and NP problems, consider UC Berkeley's course on algorithm design and analysis [UCB CS170: Efficient Algorithms and Intractable Problems](数据结构与算法/CS170.md) or MIT's advanced algorithms course [MIT 6.046: Design and Analysis of Algorithms](数据结构与算法/6.046.md).
+
+### Software Engineering
+
+#### Introductory Course
+
+There is a fundamental difference between “working” code and high-quality industrial code. Therefore, I highly recommend senior students to take [MIT 6.031: Software Construction](软件工程/6031.md). Based on Java, this course teaches how to write high-quality code that is **bug-resistant, clear, and easy to maintain and modify** with rich and detailed reading materials and well-designed programming exercises. From macro data structure design to minor details like how to write comments, following these details and experiences summarized by predecessors can greatly benefit your future programming career.
+
+
+
+#### Professional Course
+
+Of course, if you want to systematically take a software engineering course, I recommend UC Berkeley’s [UCB CS169: Software Engineering](软件工程/CS169.md). However, unlike most software engineering courses, this course does not involve the traditional **design and document** model that emphasizes various class diagrams, flowcharts, and document design. Instead, it adopts the **Agile Development** model, which has become popular in recent years, featuring small team rapid iterations and the **Software as a Service** model using cloud platforms.
+
+### Computer Architecture
+
+#### Introductory Course
+
+Since childhood, I've always heard that the world of computers is made of 0s and 1s, which I didn't understand but was deeply impressed by. If you also have this curiosity, consider spending one to two months learning the barrier-free computer course [Coursera: Nand2Tetris](体系结构/N2T.md). This comprehensive course starts from 0s and 1s, allowing you to build a computer by hand and run a Tetris game on it. It covers compilation, virtual machines, assembly, architecture, digital circuits, logic gates, etc., from top to bottom, from software to hardware. Its difficulty is carefully designed to omit many complex details of modern computers, extracting the most core essence, aiming to make it understandable to everyone. In lower levels, establishing a bird's-eye view of the entire computer system is very beneficial.
+
+#### Professional Course
+
+Of course, if you want to delve into the complex details of modern computer architecture, you still need to take a university-level course [UCB CS61C: Great Ideas in Computer Architecture](体系结构/CS61C.md). This course emphasizes practice, and you will hand-write assembly to construct neural networks in projects, build a CPU from scratch, and more, all of which will give you a deeper understanding of computer architecture, beyond the monotony of "fetch, decode, execute, memory access, write back."
+
+### Introduction to Computer Systems
+
+Computer systems are a vast and profound topic. Before delving into a specific area, having a macro conceptual understanding of each field and some general design principles will reinforce core and even philosophical concepts in your subsequent in-depth study, rather than being shackled by complex internal details and various tricks. In my opinion, the key to learning systems is to grasp these core concepts to design and implement your own systems.
+
+[MIT6.033: System Engineering](http://web.mit.edu/6.033/www/) is MIT's introductory course to systems, covering topics like operating systems, networks, distributed systems, and system security. In addition to the theory, this course also teaches some writing and expression skills, helping you learn how to design, introduce, and analyze your own systems. The accompanying textbook *Principles of Computer System Design: An Introduction* is also very well written and recommended for reading.
+
+[CMU 15-213: Introduction to Computer System](计算机系统基础/CSAPP.md) is CMU’s introductory systems course, covering architecture, operating systems, linking, parallelism, networks, etc., with both breadth and depth. The accompanying textbook *Computer Systems: A Programmer's Perspective* is also of very high quality and strongly recommended for reading.
+
+### Operating Systems
+
+> There’s nothing like writing your own kernel to deepen your understanding of operating systems.
+
+Operating systems provide a set of elegant abstractions to virtualize various complex underlying hardware, providing rich functional support for all application software. Understanding the design principles and internal mechanisms of operating systems is greatly beneficial for a programmer who is not satisfied with just being a coder. Out of love for operating systems, I have taken many operating system courses in different colleges, each with its own focus and merits. You can choose based on your interests.
+
+[MIT 6.S081: Operating System Engineering](操作系统/MIT6.S081.md), offered by the famous PDOS lab at MIT, features 11 projects that modify an elegantly implemented Unix-like operating system xv6. This course made me realize that systems is not about reading PPTs; it's about writing tens of thousands of lines of code.
+
+[UCB CS162: Operating System](操作系统/CS162.md), UC Berkeley’s operating system course, uses the same Project as Stanford — an educational operating system, Pintos. As the teaching assistant for Peking University’s 2022 and 2023 Spring Semester Operating Systems Course, I introduced and improved this Project. The course resources are fully open-sourced, with details on [the course website](https://pku-os.github.io).
+
+[NJU: Operating System Design and Implementation](操作系统/NJUOS.md), offered by Professor Yanyan Jiang at Nanjing University, provides an in-depth and accessible explanation of various operating system concepts, combining a unique system perspective with rich code examples. All course content is in Chinese, making it very convenient for students.
+
+[HIT OS: Operating System](操作系统/HITOS.md), taught by Professor Zhijun Li at Harbin Institute of Technology, is a Chinese course on operating systems. Based on the Linux 0.11 source code, the course places great emphasis on code practice, explaining the intricacies of operating systems from the student's perspective.
+
+### Parallel and Distributed Systems
+
+In recent years, the most common phrase heard in CS lectures is "Moore's Law is coming to an end." As single-core capabilities reach their limits, multi-core and many-core architectures are becoming increasingly important. The changes in hardware necessitate adaptations and changes in the upper-level programming logic. Writing parallel programs has nearly become a mandatory skill for programmers to fully utilize hardware performance. Meanwhile, the rise of deep learning has brought unprecedented demands on computing power and storage, making the deployment and optimization of large-scale clusters a hot topic.
+
+#### Parallel Computing
+
+[CMU 15-418 / Stanford CS149: Parallel Computing](并行与分布式系统/CS149.md) takes you deep into the design principles and trade-offs of modern parallel computing architectures. The course teaches you how to fully leverage hardware resources and software programming frameworks—such as CUDA, MPI, and OpenMP—to write high-performance parallel programs.
+
+#### Distributed Systems
+
+[MIT 6.824: Distributed Systems](并行与分布式系统/MIT6.824.md), like MIT 6.S081, is offered by MIT’s renowned PDOS (Parallel and Distributed Operating Systems) lab. The course is taught by Professor Robert Morris, who was once a legendary hacker—famously known for creating the first computer worm, the Morris Worm.
+
+Each lecture focuses on an in-depth reading of a classic paper in the field of distributed systems, through which the course conveys essential principles and key techniques for designing and implementing distributed systems. The course is also famous for its challenging projects: over the course of four progressively difficult programming assignments, students build a key-value store framework based on the Raft consensus algorithm. These projects offer a firsthand experience of the randomness and complexity brought by concurrency and distribution—often felt most acutely during painful debugging sessions.
+
+### System Security
+
+Whether you chose computer science because of a youthful dream of becoming a hacker, the reality is that becoming a hacker is a long and difficult journey.
+
+#### Theoretical Courses
+
+[UCB CS161: Computer Security](系统安全/CS161.md) at UC Berkeley covers stack attacks, cryptography, website security, network security, and more.
+
+[SU SEED Labs](系统安全/SEEDLabs.md) at Syracuse University, supported by a $1.3 million grant from the NSF, has developed hands-on experimental exercises (called SEED Labs) for cybersecurity education. The course emphasizes both theoretical teaching and practical exercises, including detailed open-source lectures, video tutorials, textbooks (printed in multiple languages), and a ready-to-use virtual machine and Docker-based attack-defense environment. This project is currently used by 1,050 institutions worldwide and covers a wide range of topics in computer and information security, including software security, network security, web security, operating system security, and mobile app security.
+
+#### Practical Courses
+
+After mastering this theoretical knowledge, it's essential to cultivate and hone these "hacker skills" in practice. [CTF competitions](https://ctf-wiki.org/) are a popular way to comprehensively test your understanding and application of computer knowledge in various fields. Peking University also successfully held the [0th and 1st editions](https://geekgame.pku.edu.cn/), encouraging participation to improve skills through practice. Here are some resources I use for learning (and relaxing):
+
+- [CTF-wiki](https://ctf-wiki.org/)
+- [CTF-101](https://ctf101.org/)
+- [Hacker-101](https://ctf.hacker101.com/)
+
+### Computer Networks
+
+> There’s nothing like writing your own TCP/IP protocol stack to deepen your understanding of computer networks.
+
+The renowned [Stanford CS144: Computer Network](计算机网络/CS144.md) includes 8 projects that guide you in implementing the entire TCP/IP protocol stack.
+
+If you're mainly interested in gaining a theoretical understanding of computer networks, it's recommended to read the [textbook](https://textbook.cs168.io/) that accompanies the course [UCB CS168](计算机网络/CS168.md).
+
+
+### Database Systems
+
+> There’s nothing like building your own relational database to deepen your understanding of database systems.
+
+CMU's famous database course [CMU 15-445: Introduction to Database System](数据库系统/15445.md) guides you through 4 projects to add various functionalities to the educational relational database [bustub](https://github.com/cmu-db/bustub). The experimental evaluation framework is also open-source, making it very suitable for self-learning. The course experiments also use many new features of C++11, offering a great opportunity to strengthen C++ coding skills.
+
+Berkeley, as the birthplace of the famous open-source database PostgreSQL, has its own course [UCB CS186: Introduction to Database System](数据库系统/CS186.md) where you will implement a relational database in Java that supports SQL concurrent queries, B+ tree indexing, and fault recovery.
+
+### Compiler Theory
+
+> There’s nothing like writing your own compiler to deepen your understanding of compilers.
+
+[Stanford CS143: Compilers](编译原理/CS143.md) guides you through the process of writing a compiler.
+
+### Web Development
+
+Front-end and back-end development are often overlooked in standard computer science curricula, but in reality, having these skills can be extremely beneficial—for example, creating your own personal website or building a polished demo page for a course project.
+
+If you're looking for a quick, two-week crash course, I recommend the [MIT Web Development Course](Web开发/mitweb.md). For a more comprehensive and structured learning experience, check out [Stanford CS142: Web Applications](Web开发/CS142.md).
+
+### Computer Graphics
+
+I personally don't have much background in computer graphics, so I've collected a selection of high-quality courses recommended by the community for those interested in exploring the field.
+
+- [Stanford CS148](计算机图形学/CS148.md)
+- [Games101](计算机图形学/GAMES101.md)
+- [Games103](计算机图形学/GAMES103.md)
+- [Games202](计算机图形学/GAMES202.md)
+
+### Data Science
+
+Data science, machine learning, and deep learning are closely related, with a focus on practical application. Berkeley's [UCB Data100: Principles and Techniques of Data Science](数据科学/Data100.md) lets you master various data analysis tools and algorithms through extensive programming exercises. The course guides you through extracting desired results from massive datasets and making predictions about future data or user behavior. For those looking to learn industrial-level data mining and analysis techniques, Stanford's big data mining course [CS246: Mining Massive Data Sets](https://web.stanford.edu/class/cs246/) is an option.
+
+### Artificial Intelligence
+
+Artificial intelligence has been one of the hottest fields in computer science over the past decade. If you're not content with just hearing about AI advancements in the media and want to delve into the subject, I highly recommend Harvard's renowned CS50 series AI course [Harvard CS50: Introduction to AI with Python](人工智能/CS50.md). The course is concise and covers several major branches of traditional AI, supplemented with rich and interesting Python programming exercises to reinforce your understanding of AI algorithms. However, the content is somewhat simplified for online learners and doesn't delve into deep mathematical theories. For a more systematic and in-depth study, consider an undergraduate-level course like Berkeley's [UCB CS188: Introduction to Artificial Intelligence](人工智能/CS188.md). This course's projects feature the classic game "Pac-Man," allowing you to use AI algorithms to play the game, which is very fun.
+
+### Machine Learning
+
+The most significant recent progress in the field of machine learning is the emergence of deep learning, a branch based on deep neural networks. However, many algorithms based on statistical learning are still widely used in data analysis. If you're new to machine learning and don't want to get bogged down in complex mathematical proofs, start with Andrew Ng's (Enda Wu) [Coursera: Machine Learning](机器学习/ML.md). This course is well-known in the field of machine learning, and Enda Wu, with his profound theoretical knowledge and excellent presentation skills, makes many complex algorithms accessible and practical. The accompanying assignments are also of high quality, helping you get started quickly.
+
+However, completing this course will only give you a general understanding of the field of machine learning. To truly understand the mathematical principles behind these "magical" algorithms or to engage in related research, you need a more "mathematical" course, such as [Stanford CS229: Machine Learning](机器学习/CS229.md) or [UCB CS189: Introduction to Machine Learning](机器学习/CS189.md).
+
+If you plan to pursue scientific research in machine learning theory, you can refer to the [advanced learning roadmap](./机器学习进阶/roadmap.md) shared by [Yao Fu](https://franxyao.github.io/), which includes more in-depth, graduate-level courses.
+
+
+### Deep Learning
+
+The popularity of AlphaGo a few years ago brought deep learning to the public eye, leading many universities to establish related majors. Many other areas of computer science also use deep learning technology for research, so regardless of your field, you will likely encounter some needs related to neural networks and deep learning. For a quick introduction, I again recommend Andrew Ng's (Enda Wu) [Coursera: Deep Learning](深度学习/CS230.md), a top-rated course on Coursera. Additionally, if you find English-language courses challenging, consider Professor Hongyi Li's course [National Taiwan University: Machine Learning](深度学习/LHY.md). Although titled "Machine Learning," this course covers almost all areas of deep learning and is very comprehensive, making it suitable for getting a broad overview of the field. The professor is also very humorous, with frequent witty remarks in class.
+
+Due to the rapid development of deep learning, there are now many research branches. For further in-depth study, consider the following representative courses:
+
+#### Computer Vision
+
+- [UMich EECS 498-007 / 598-005: Deep Learning for Computer Vision](深度学习/EECS498-007.md)
+- [Stanford CS231n: CNN for Visual Recognition](深度学习/CS231.md)
+
+#### Natural Language Processing
+
+- [Stanford CS224n: Natural Language Processing](深度学习/CS224n.md)
+
+#### Graph Neural Networks
+
+- [Stanford CS224w: Machine Learning with Graphs](深度学习/CS224w.md)
+
+#### Reinforcement Learning
+
+- [UCB CS285: Deep Reinforcement Learning](深度学习/CS285.md)
+
+### Deep Learning Systems
+
+As deep learning models grow in importance and demand increasing computational resources, optimizing the underlying systems for training and inference has become increasingly critical. For those looking to enter this field, a highly recommended resource is [CMU 10-414/714: Deep Learning Systems](./机器学习系统/CMU10-414.md). This course provides a comprehensive "full-stack" understanding of deep learning systems—from high-level architectural design of modern frameworks, to the principles and implementation of automatic differentiation, down to low-level hardware acceleration and real-world deployment.
+
+To deepen theoretical understanding, students are tasked with building a deep learning library from scratch, called Needle, as part of the coursework. This library supports automatic differentiation on computational graphs, GPU-based acceleration, and includes modules for loss functions, data loaders, and optimizers. On top of this, students will implement several common neural network architectures including CNNs, RNNs, LSTMs, and Transformers.
+
+For those with foundational knowledge, the next step would be to explore [MIT 6.5940: TinyML and Efficient Deep Learning Computing](./机器学习系统/EML.md), taught by [Professor Song Han](https://hanlab.mit.edu/songhan). This course dives into techniques for making neural networks more efficient, such as pruning, quantization, distillation, and neural architecture search. It also covers cutting-edge system optimizations for advanced models, including large language models.
+
+### Deep Generative Models
+
+With the explosive popularity of large language models, understanding the principles behind them is essential to staying at the forefront of the field. You can refer to my recommended [learning roadmap](./深度生成模型/roadmap.md) for a guided approach to studying this area.
+
+## Customize Your Course Map
+
+> Better to teach fishing than to give fish.
+
+The course map above inevitably carries strong personal preferences and may not suit everyone. It is more intended to serve as a starting point for exploration. If you want to select your own areas of interest for study, you can refer to the following resources:
+
+- [MIT OpenCourseWare](https://ocw.mit.edu/): MIT's open-sharing project for course resources, featuring thousands of courses from various disciplines, including computer science courses numbered 6.xxx.
+- [MIT CS Course List](http://student.mit.edu/catalog/m6a.html): List of CS courses at MIT.
+- [UC Berkeley EECS Course Map](https://hkn.eecs.berkeley.edu/courseguides): UC Berkeley's EECS curriculum plan, presenting the categories and prerequisites of various courses in a course map format, most of which are included in this book.
+- [UC Berkeley CS Course List](https://www2.eecs.berkeley.edu/Courses/CS/): List of CS courses at UC Berkeley.
+- [Stanford CS Course List](https://blog.csdn.net/qq_41220023/article/details/81976967): List of CS courses at Stanford.

docs/CS学习规划.md

@@ -30,7 +30,7 @@ IDE (Integrated Development Environment)：集成开发环境，说白了就是
 
 [CMake](必学工具/CMake.md)：一款功能比 GNU Make 更为强大的构建工具，建议掌握 GNU Make 之后再加以学习。
 
-[LaTex](必学工具/LaTeX.md)：<del>逼格提升</del> 论文排版工具。
+[LaTeX](必学工具/LaTeX.md)：<del>逼格提升</del> 论文排版工具。
 
 [Docker](必学工具/Docker.md)：一款相较于虚拟机更轻量级的软件打包与环境部署工具。
 
@@ -52,7 +52,7 @@ IDE (Integrated Development Environment)：集成开发环境，说白了就是
 
 ### PC 端环境配置
 
-如果你是 Mac 用户，那么你很幸运，这份[指南](https://sourabhbajaj.com/mac-setup/) 将会手把手地带你搭建起整套开发环境。如果你是 Windows 用户，在开源社区的努力下，你同样可以获得与其他平台类似的体验：[Scoop](/必学工具/Scoop)。
+如果你是 Mac 用户，那么你很幸运，这份[指南](https://sourabhbajaj.com/mac-setup/) 将会手把手地带你搭建起整套开发环境。如果你是 Windows 用户，在开源社区的努力下，你同样可以获得与其他平台类似的体验：[Scoop](必学工具/Scoop.md)。
 
 另外大家可以参考一份灵感来自 [6.NULL MIT-Missing-Semester](编程入门/MIT-Missing-Semester.md) 的 [环境配置指南][guide]，重点在于终端的美化配置。此外还包括常用软件源（如 GitHub, Anaconda, PyPI 等）的加速与替换以及一些 IDE 的配置与激活教程。
 
@@ -141,28 +141,33 @@ IDE (Integrated Development Environment)：集成开发环境，说白了就是
 
 > 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
+#### General
 
 - [MIT-Missing-Semester](编程入门/MIT-Missing-Semester.md)
+- [Harvard CS50: This is CS50x](编程入门/C/CS50.md)
+
+#### Java
+
+- [MIT 6.092: Introduction To Programming In Java](编程入门/Java/MIT%206.092.md)
 
 #### Python
 
-- [CS50P: CS50's Introduction to Programming with Python](编程入门/CS50P.md)
-- [Harvard CS50: This is CS50x](编程入门/CS50.md)
-- [UCB CS61A: Structure and Interpretation of Computer Programs](编程入门/CS61A.md)
+- [CS50P: CS50's Introduction to Programming with Python](编程入门/Python/CS50P.md)
+- [UCB CS61A: Structure and Interpretation of Computer Programs](编程入门/Python/CS61A.md)
+- [MIT6.100L: Introduction to CS and Programming using Python](编程入门/Python/MIT6.100L.md)
 
 #### C++
 
-- [Stanford CS106B/X: Programming Abstractions](编程入门/CS106B_CS106X.md)
-- [Stanford CS106L: Standard C++ Programming](编程入门/CS106L.md)
+- [Stanford CS106B/X: Programming Abstractions](编程入门/cpp/CS106B_CS106X.md)
+- [Stanford CS106L: Standard C++ Programming](编程入门/cpp/CS106L.md)
 
 #### Rust
 
-- [Stanford CS110L: Safety in Systems Programming](编程入门/CS110L.md)
+- [Stanford CS110L: Safety in Systems Programming](编程入门/Rust/CS110L.md)
 
 #### OCaml
 
-- [Cornell CS3110 textbook: Functional Programming in OCaml](https://cs3110.github.io/textbook/cover.html)
+- [Cornell CS3110: OCaml Programming Correct + Efficient + Beautiful](编程入门/Functional/CS3110.md)
 
 ### 电子基础
 
@@ -184,7 +189,7 @@ IDE (Integrated Development Environment)：集成开发环境，说白了就是
 
 算法是计算机科学的核心，也是几乎一切专业课程的基础。如何将实际问题通过数学抽象转化为算法问题，并选用合适的数据结构在时间和内存大小的限制下将其解决是算法课的永恒主题。如果你受够了老师的照本宣科，那么我强烈推荐伯克利的 [UCB CS61B: Data Structures and Algorithms](数据结构与算法/CS61B.md) 和普林斯顿的 [Coursera: Algorithms I & II](数据结构与算法/Algo.md)，这两门课的都讲得深入浅出并且会有丰富且有趣的编程实验将理论与知识结合起来。
 
-以上两门课程都是基于 Java 语言，如果你想学习 C/C++ 描述的版本，可以参考斯坦福的数据结构与基础算法课程 [Stanford CS106B/X: Programming Abstractions](编程入门/CS106B_CS106X.md)。偏好 Python 的同学可以学习 MIT 的算法入门课 [MIT 6.006: Introduction to Algorithms](数据结构与算法/6.006.md)
+以上两门课程都是基于 Java 语言，如果你想学习 C/C++ 描述的版本，可以参考斯坦福的数据结构与基础算法课程 [Stanford CS106B/X: Programming Abstractions](编程入门/cpp/CS106B_CS106X.md)。偏好 Python 的同学可以学习 MIT 的算法入门课 [MIT 6.006: Introduction to Algorithms](数据结构与算法/6.006.md)
 
 对一些更高级的算法以及 NP 问题感兴趣的同学可以学习伯克利的算法设计与分析课程 [UCB CS170: Efficient Algorithms and Intractable Problems](数据结构与算法/CS170.md) 或者 MIT 的高阶算法 [MIT 6.046: Design and Analysis of Algorithms](数据结构与算法/6.046.md)。
 
@@ -214,7 +219,7 @@ IDE (Integrated Development Environment)：集成开发环境，说白了就是
 
 [MIT6.033: System Engineering](http://web.mit.edu/6.033/www/) 是 MIT 的系统入门课，主题涉及了操作系统、网络、分布式和系统安全，除了知识点的传授外，这门课还会讲授一些写作和表达上的技巧，让你学会如何设计并向别人介绍和分析自己的系统。这本书配套的教材 *Principles of Computer System Design: An Introduction* 也写得非常好，推荐大家阅读。
 
-[CMU 15-213: Introduction to Computer System](体系结构/CSAPP.md) 是 CMU 的系统入门课，内容覆盖了体系结构、操作系统、链接、并行、网络等等，兼具广度和深度，配套的教材 *Computer Systems: A Programmer's Perspective* 也是质量极高，强烈建议阅读。
+[CMU 15-213: Introduction to Computer System](计算机系统基础/CSAPP.md) 是 CMU 的系统入门课，内容覆盖了体系结构、操作系统、链接、并行、网络等等，兼具广度和深度，配套的教材 *Computer Systems: A Programmer's Perspective* 也是质量极高，强烈建议阅读。
 
 ### 操作系统
 
@@ -236,11 +241,11 @@ IDE (Integrated Development Environment)：集成开发环境，说白了就是
 
 #### 并行计算
 
-[CMU 15-418/Stanford CS149: Parallel Computing](并行与分布式系统/CS149.md)
+[CMU 15-418/Stanford CS149: Parallel Computing](并行与分布式系统/CS149.md) 会带你深入理解现代并行计算架构的设计原则与必要权衡，并学会如何充分利用硬件资源以及软件编程框架（例如 CUDA，MPI，OpenMP 等）编写高性能的并行程序。
 
 #### 分布式系统
 
-[MIT 6.824: Distributed System](并行与分布式系统/MIT6.824.md)
+[MIT 6.824: Distributed System](并行与分布式系统/MIT6.824.md) 和 MIT 6.S081 一样，出品自 MIT 大名鼎鼎的 PDOS 实验室，授课老师 Robert Morris 教授曾是一位顶尖黑客，世界上第一个蠕虫病毒 Morris 病毒就是出自他之手。这门课每节课都会精读一篇分布式系统领域的经典论文，并由此传授分布式系统设计与实现的重要原则和关键技术。同时其课程 Project 也是以难度之大而闻名遐迩，4 个编程作业循序渐进带你实现一个基于 Raft 共识算法的 KV-store 框架，让你在痛苦的 debug 中体会并行与分布式带来的随机性和复杂性。
 
 ### 系统安全
 
@@ -250,13 +255,11 @@ IDE (Integrated Development Environment)：集成开发环境，说白了就是
 
 [UCB CS161: Computer Security](系统安全/CS161.md) 是伯克利的系统安全课程，会涵盖栈攻击、密码学、网站安全、网络安全等等内容。
 
-[ASU CSE365: Introduction to Cybersecurity](系统安全/CSE365.md) 亚利桑那州立大学的 Web 安全课程，主要涉及注入、汇编与密码学的内容。
+[SU SEED Labs](系统安全/SEEDLabs.md) 是雪城大学的网安课程，由 NSF 提供130万美元的资金支持，为网安教育开发了动手实践性的实验练习（称为 SEED Lab）。课程理论教学和动手实践并重，包含详细的开源讲义、视频教程、教科书（被印刷为多种语言）、开箱即用的基于虚拟机和 docker 的攻防环境等。目前全球有1050家研究机构在使用该项目。涵盖计算机和信息安全领域的广泛主题，包括软件安全、网络安全、Web 安全、操作系统安全和移动应用安全。
 
-[ASU CSE466: Computer Systems Security](系统安全/CSE466.md) 亚利桑那州立大学的系统安全课程，涉及内容全面。门槛较高，需要对 Linux, C 与 Python 充分熟悉。
+#### CTF 实践
 
-#### 实践课程
-
-掌握这些理论知识之后，还需要在实践中培养和锻炼这些“黑客素养”。[CTF 夺旗赛](https://ctf-wiki.org/)是一项比较热门的系统安全比赛，赛题中会融会贯通地考察你对计算机各个领域知识的理解和运用。北大今年也成功举办了[第 0 届和第 1 届](https://geekgame.pku.edu.cn/)，鼓励大家后期踊跃参与，在实践中提高自己。下面列举一些我平时学习（摸鱼）用到的资源：
+掌握这些理论知识之后，还需要在实践中培养和锻炼这些“黑客素养”。[CTF 夺旗赛](https://ctf-wiki.org/)是一项比较热门的系统安全比赛，赛题中会融会贯通地考察你对计算机各个领域知识的理解和运用。北大每年会举办[相关赛事](https://geekgame.pku.edu.cn/)，鼓励大家踊跃参与，在实践中提高自己。下面列举一些我平时学习（摸鱼）用到的资源：
 
 - [CTF-wiki](https://ctf-wiki.org/)
 - [CTF-101](https://ctf101.org/)
@@ -268,7 +271,7 @@ IDE (Integrated Development Environment)：集成开发环境，说白了就是
 
 大名鼎鼎的 [Stanford CS144: Computer Network](计算机网络/CS144.md)，8 个 Project 带你实现整个 TCP/IP 协议栈。
 
-如果你只是想在理论上对计算机网络有所了解，那么推荐计网著名教材《自顶向下方法》的配套学习资源 [Computer Networking: A Top-Down Approach](计算机网络/topdown.md)。
+如果你只是想在理论上对计算机网络有所了解，那么推荐阅读 [UCB CS168](计算机网络/CS168.md) 这门课程配套的[教材](https://textbook.cs168.io/)。
 
 ### 数据库系统
 
@@ -282,22 +285,16 @@ Berkeley 作为著名开源数据库 postgres 的发源地也不遑多让，[UCB
 
 > 没有什么能比自己写个编译器更能加深对编译器的理解了。
 
-[Stanford CS143: Compilers](编译原理/CS143.md) 带你手写编译器。
+理论学习推荐阅读大名鼎鼎的《龙书》。当然动手实践才是掌握编译原理最好的方式，推荐[北京大学编译原理实践](./编译原理/PKU-Compilers.md)课程，丰富的实验配套和循序渐进的文档带你实现一个类C语言到 RISC-V 汇编的编译器。当然编译原理课程目录下也有众多其他优质实验供你选择。
 
-### Web开发
+### Web 开发
 
-前后端开发很少在计算机的培养方案里被重视，但其实掌握这项技能还是好处多多的，例如搭建自己的个人主页，抑或是给自己的课程项目做一个精彩的展示网页。
-
-#### 两周速成版
-
-[MIT web development course](Web开发/mitweb.md)
-
-#### 系统学习版
-
-[Stanford CS142: Web Applications](Web开发/CS142.md)
+前后端开发很少在计算机的培养方案里被重视，但其实掌握这项技能还是好处多多的，例如搭建自己的个人主页，抑或是给自己的课程项目做一个精彩的展示网页。如果你只是想两周速成，那么推荐 [MIT web development course](Web开发/mitweb.md)。如果想系统学习，推荐 [Stanford CS142: Web Applications](Web开发/CS142.md)。
 
 ### 计算机图形学
 
+我本人对计算机图形学了解不多，这里收录了一些社区推荐的优质课程供大家选择：
+
 - [Stanford CS148](计算机图形学/CS148.md)
 - [Games101](计算机图形学/GAMES101.md)
 - [Games103](计算机图形学/GAMES103.md)
@@ -318,14 +315,18 @@ Berkeley 作为著名开源数据库 postgres 的发源地也不遑多让，[UCB
 
 但上过这门课只能让你从宏观上对机器学习这一领域有一定了解，如果想真正理解那些“神奇”算法背后的数学原理甚至从事相关领域的科研工作，那么还需要一门更“数学”的课程，例如 [Stanford CS229: Machine Learning](机器学习/CS229.md) 或者 [UCB CS189: Introduction to Machine Learning](机器学习/CS189.md)。
 
+当然，如果你之后致力于从事机器学习理论相关的科学研究，那么可以参考 [Yao Fu](https://franxyao.github.io/) 分享的[进阶学习路线](./机器学习进阶/roadmap.md)学习一些更深入的研究生难度的课程。
+
 ### 深度学习
 
-前几年 AlphaGo 的大热让深度学习进入了大众的视野，不少大学甚至专门成立了相关专业。很多计算机的其他领域也会借助深度学习的技术来做研究，因此基本不管你干啥多少都会接触到一些神经网络、深度学习相关的技术需求。如果想快速入门，同样推荐 Andrew Ng （吴恩达）的 [Coursera: Deep Learning](深度学习/CS230.md)，质量无需多言，Coursera 上罕见的满分课程。此外如果你觉得英文课程学习起来有难度，推荐李宏毅老师的 [国立台湾大学：机器学习](深度学习/LHY.md) 课程。这门课打着机器学习的名号，却囊括了深度学习领域的几乎所有方向，非常全面，很适合你从宏观上对这个领域有一个大致的了解。而且老师本人也非常幽默，课堂金句频出。
+前几年 AlphaGo 的大热让深度学习进入了大众的视野，不少大学专门成立了相关专业。很多计算机的其他领域也会借助深度学习的技术来做研究，因此基本不管你干啥多少都会接触到一些神经网络、深度学习相关的技术需求。如果想快速入门，同样推荐 Andrew Ng （吴恩达）的 [Coursera: Deep Learning](深度学习/CS230.md)，质量无需多言，Coursera 上罕见的满分课程。此外如果你觉得英文课程学习起来有难度，推荐李宏毅老师的 [国立台湾大学：机器学习](深度学习/LHY.md) 课程。这门课打着机器学习的名号，却囊括了深度学习领域的几乎所有方向，非常全面，很适合你从宏观上对这个领域有一个大致的了解。而且老师本人也非常幽默，课堂金句频出。
 
-当然因为深度学习领域发展非常迅速，已经拥有了众多研究分支，如果想要进一步深入，可以按需学习下面罗列的代表课程，
+当然因为深度学习领域发展非常迅速，已经拥有了众多研究分支，如果想要进一步深入，可以按需学习下面罗列的代表课程:
 
 #### 计算机视觉
 
+[UMich EECS 498-007 / 598-005: Deep Learning for Computer Vision](深度学习/EECS498-007.md)
+
 [Stanford CS231n: CNN for Visual Recognition](深度学习/CS231.md)
 
 #### 自然语言处理
@@ -340,6 +341,17 @@ Berkeley 作为著名开源数据库 postgres 的发源地也不遑多让，[UCB
 
 [UCB CS285: Deep Reinforcement Learning](深度学习/CS285.md)
 
+
+### 深度学习系统
+
+随着深度学习模型的重要性和资源需求越来越大，针对其训练和推理相关的底层系统优化也越发重要。如果想入门这个领域，推荐 [CMU 10-414/714: Deep Learning Systems](./机器学习系统/CMU10-414.md)，内容覆盖了深度学习系统“全栈”的知识体系。从现代深度学习系统框架的顶层设计，到自微分算法的原理和实现，再到底层硬件加速和实际生产部署。为了更好地掌握理论知识，学生将会在课程作业中从头开始设计和实现一个完整的深度学习库 Needle，使其能对计算图进行自动微分，能在 GPU 上实现硬件加速，并且支持各类损失函数、数据加载器和优化器。在此基础上，学生将实现几类常见的神经网络，包括 CNN，RNN，LSTM，Transformer 等等。有一定基础后，还可以学习 [Song Han](https://hanlab.mit.edu/songhan) 老师开设的 [MIT6.5940: TinyML and Efficient Deep Learning Computing](./机器学习系统/EML.md) 课程，了解让神经网络轻量化的各种关键技术，例如剪枝、量化、蒸馏、网络架构搜索等等。此外，课程中还会涉及很多更前沿的深度学习模型例如大语言模型相关的系统优化。
+
+### 深度生成模型
+
+随着大语言模型的爆火，了解其背后的原理才能紧跟时代潮流。可以参考笔者推荐的[学习路线](./深度生成模型/roadmap.md)进行学习。
+
+
+
 ## <a id="yourmap">定制属于你的课程地图</a>
 
 > 授人以鱼不如授人以渔。

docs/Web开发/CS571.en.md

@@ -16,7 +16,7 @@ Although this course doesn't require prior knowledge of Javascript/HTML/CSS, the
 
 This course also includes an introduction to and practices for Dialog Flow, a ChatBot development tool by Google. You can also find content related to UX development (on the practical side) in this course.
 
-All course materials and assignments are open-source, but you will need to request an X-CS571-ID header from the instructor, Cole Nelson (ctnelson2@wisc.edu). The header will be necessary for API request. When sending an email, it is advisable to include a brief self-introduction. It is unclear whether the instructor is willing to give everyone an ID. If you got turned down, please [raise an issue for this GitHub repo](https://github.com/PKUFlyingPig/cs-self-learning/issues/new/choose).
+According to the official website, CS 571 is open to everyone. You can request a Badger ID directly from the [webpage](https://cs571.org/auth) using your email address.
 
 ## Course Resources
 

docs/Web开发/CS571.md

@@ -16,10 +16,10 @@
 
 此外，本课程还对 Google 旗下的 ChatBot 开发工具 Dialog Flow 有较为深入的介绍和练习。还对 UX Design 的实用原则和技术有所讲解。
 
-所有课程资料和作业都是开源的，但你需要向授课教师 Cole Nelson (ctnelson2@wisc.edu) 发送电子邮件以获取 X-CS571-ID。该 ID 是向 API 发送 request 必需。在发送邮件时，建议附上自我介绍。目前还不清楚老师是否愿意给所有人提供ID，如果老师表示无法分享，请[在 GitHub repo 里提一个 issue](https://github.com/PKUFlyingPig/cs-self-learning/issues/new/choose)。
+根据官网信息，CS 571 对所有人开放。你可以在[官网](https://cs571.org/auth)直接使用电子邮箱申请 Badger ID。
 
 ## 课程资源
 
 - 课程网站：<https://cs571.org>
 - 课程视频：请参考课程网站上标有“R”的链接
-- 课程作业：请参考课程网站上的相关信息
+- 课程作业：请参考课程网站上的相关信息

docs/index.en.md

@@ -2,9 +2,7 @@
   ![Image title](./images/title.png){ width="600" }
 </figure>
 
-# Foreword
-
-**The English version is still under development, please check [this issue](https://github.com/PKUFlyingPig/cs-self-learning/issues/222) if you want to contribute.**
+# **Foreword**
 
 This is a self-learning guide to computer science, and a memento of my three years of self-learning at university.
 
@@ -12,12 +10,13 @@ It is also a gift to the young students at Peking University. It would be a grea
 
 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](mailto:zhongyinmin@pku.edu.cn) or ask questions in the issue).
 
+- User guide for this book: Given the numerous resources covered in this book, I have developed corresponding usage guides based on different people's free time and learning objectives.
+- A reference CS learning plan: This is a comprehensive and systematic CS self-learning plan that I have formulated based on my own self-study experience.
 - 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.
+- **List of high quality CS courses**: I will summarize all the high quality foreign CS courses I have taken and the community contributed into different categories and give relevant self-learning advice. Most of them will have a separate repository containing relevant resources as well as the homework/project implementations.
 
-## The place where dreams start —— CS61A
+## **The place where dreams start —— 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 —— 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.
 
@@ -41,9 +40,9 @@ Imagine that if someone could chew up the hard knowledge and present it to you i
 
 If you think I'm exaggerating, start with [CS61A](https://cs61a.org/), because it's where my dreams began.
 
-## Why write this book?
+## **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](https://github.com/PKUFlyingPig/Self-learning-Computer-Science) 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.
+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](https://github.com/PKUFlyingPig/Self-learning-Computer-Science) 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.
 
@@ -51,9 +50,9 @@ In my last college year, when I opened up my curriculum book, I realized that it
 
 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.
+I firmly believe that if you have read to this line, you do not lack the ability and commitment 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
+## **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.
 
@@ -61,7 +60,7 @@ Another great thing about self-learning is that you can learn from different per
 
 A third advantage of self-learning is that you do not need to go to the class, listening to the boring lectures.
 
-## Cons
+## **Cons**
 
 Of course, as a big fan of self-learning, I have to admit that it has its disadvantages.
 
@@ -71,16 +70,16 @@ The second thing is that these courses are basically in English. From the videos
 
 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?
+## **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
+## **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?
+## **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](mailto:zhongyinmin@pku.edu.cn)).

docs/index.md

@@ -4,7 +4,7 @@
 
 # 前言
 
-**最近更新：[Release v1.0.1](https://github.com/PKUFlyingPig/cs-self-learning/releases/tag/v1.0.1) 已发布 🎉**
+**🎉🎉 [Release v1.2.0](https://github.com/PKUFlyingPig/cs-self-learning/releases/tag/v1.2.0): 更新了[深度生成模型学习路线](./深度生成模型/roadmap.md) 🎉🎉**
 
 这是一本计算机的自学指南，也是对自己大学三年自学生涯的一个纪念。
 
@@ -12,10 +12,11 @@
 
 本书目前包括了以下部分(如果你有其他好的建议，或者想加入贡献者的行列，欢迎邮件 [zhongyinmin@pku.edu.cn](mailto:zhongyinmin@pku.edu.cn) 或者在 issue 里提问)：
 
-- 必学工具：IDE, 翻墙, StackOverflow, Git, GitHub, Vim, LaTeX, GNU Make, 实用工具 ...
-- 环境配置：PC端以及服务器端开发环境配置、各类运维相关教材及资料 ...
-- 经典书籍推荐：看过 CSAPP 这本书的同学一定感叹好书的重要，我将列举推荐自己看过的计算机领域的必看好书与资源链接。
-- **国外高质量 CS 课程汇总**：我将把我上过的所有高质量的国外 CS 课程分门别类进行汇总，并给出相关的自学建议，大部分课程都会有一个独立的仓库维护相关的资源以及我的作业实现。
+- 本书使用指南：由于书内涵盖资源众多，我根据不同人群的空闲时间和学习目标制定了对应的使用指南。
+- 一份供参考的 CS 学习规划：我根据自己的自学经历制定的全面的、系统化的 CS 自学规划。
+- 必学工具：一些 CSer 效率工具介绍，例如 IDE, 翻墙, StackOverflow, Git, GitHub, Vim, LaTeX, GNU Make, Docker, 工作流 等等。
+- 经典书籍推荐：你是否苦于教材的晦涩难懂不知所云？别从自己身上找原因了，可能只是教材写得太烂。看过 CSAPP 这本书的同学一定会感叹好书的重要，我将列举推荐各个计算机领域的必看好书与资源链接。
+- **国内外高质量 CS 课程汇总**：我将把我上过的以及开源社区贡献的**高质量**的国内外 CS 课程分门别类进行汇总，介绍其课程内容特点并给出相应的自学建议，大部分课程都会有一个独立的仓库维护相关的资源以及作业实现供大家学习参考。
 
 ## 梦开始的地方 —— CS61A
 
@@ -27,13 +28,13 @@
 
 为避免有崇洋媚外之嫌，我单纯从一个学生的视角来讲讲自学 CS61A 的体验：
 
-- 独立搭建的课程网站: 一个网站将所有课程资源整合一体，条理分明的课程 schedule、所有 slides, hw, discussion 的文件链接、详细明确的课程给分说明、历年的考试题与答案。这样一个网站抛开美观程度不谈，既方便学生，也让资源公正透明。
+- 独立搭建的课程网站: 一个网站将所有课程资源整合一体，条理分明的课程 schedule、所有 slides, homework, discussion 的文件链接、详细明确的课程给分说明、历年的考试题与答案。这样一个网站抛开美观程度不谈，既方便学生，也让资源公正透明。
 
-- 课程教授亲自编写的教材：CS61A 这门课的开课老师将MIT的经典教材 *Structure and Interpretation of Computer Programs* (SICP) 用Python这门语言进行改编（原教材基于 Scheme 语言），保证了课堂内容与教材内容的一致性，同时补充了更多细节，可以说诚意满满。而且全书开源，可以直接线上阅读。
+- 课程教授亲自编写的教材：CS61A 这门课的开课老师将 MIT 的经典教材 *Structure and Interpretation of Computer Programs* (SICP) 用Python这门语言进行改编（原教材基于 Scheme 语言），保证了课堂内容与教材内容的一致性，同时补充了更多细节，可以说诚意满满。而且[全书开源](https://www.composingprograms.com/)，可以直接线上阅读。
 
-- 丰富到让人眼花缭乱的课程作业：14 个 lab 巩固随堂知识点，10 个 homework，还有 4 个代码量均上千行的 project。与大家熟悉的 OJ 和 Word 文档式的作业不同，所有作业均有完善的代码框架，保姆级的作业说明。每个 Project 都有详尽的 handout 文档、全自动的评分脚本。CS61A 甚至专门开发了一个[自动化的作业提交评分系统](https://okpy.org/)（据说还发了论文）。当然，有人会说“一个 project 几千行代码大部分都是助教帮你写好的，你还能学到啥？”。此言差矣，作为一个刚刚接触计算机，连安装 Python 都磕磕绊绊的小白来说，这样完善的代码框架既可以让你专注于巩固课堂上学习到的核心知识点，又能有“我才学了一个月就能做一个小游戏了！”的成就感，还能有机会阅读学习别人高质量的代码，从而为自己所用。我觉得在低年级，这种代码框架可以说百利而无一害。唯一的害也许是苦了老师和助教，因为开发这样的作业可想而知需要相当的时间投入。
+- 丰富到让人眼花缭乱的课程作业：14 个 lab 巩固随堂知识点，10 个 homework，还有 4 个代码量均上千行的 project。与大家熟悉的 OJ 和 Word 文档式的作业不同，所有作业均有完善的代码框架，保姆级的作业说明。每个 Project 都有详尽的 handout 文档、全自动的评分脚本。CS61A 甚至专门开发了一个[自动化的作业提交评分系统](https://okpy.org/)（据说还发了论文）。当然，有人会说“一个 project 几千行代码大部分都是助教帮你写好的，你还能学到啥？”。此言差矣，作为一个刚刚接触计算机，连安装 Python 都磕磕绊绊的小白来说，这样完善的代码框架既可以让你专注于巩固课堂上学习到的核心知识点，又能有“我才学了一个月就能做一个小游戏了！”的成就感，还能有机会阅读学习别人高质量的代码，从而为自己所用。我觉得在低年级，这种代码框架可以说百利而无一害。就是苦了老师和助教，因为开发这样的作业可想而知需要相当大的时间投入和多年的迭代积累。
 
-- 每周 Discussion 讨论课，助教会讲解知识难点和考试例题：类似于北京大学 ICS 的小班研讨，但习题全部用 LaTeX 撰写，相当规范且会明确给出 solution。
+- 每周 Discussion 讨论课：助教会讲解知识难点和考试例题，习题全部用 LaTeX 撰写，相当规范并且会给出详细的解答，让学生及时查漏补缺巩固知识点。
 
 这样的课程，你完全不需要任何计算机的基础，你只需要努力、认真、花时间就够了。此前那种有劲没处使的感觉，那种付出再多时间却得不到回报的感觉，从此烟消云散。这太适合我了，我从此爱上了自学。
 
@@ -43,13 +44,13 @@
 
 ## 为什么写这本书
 
-在我2020年秋季学期担任《深入理解计算机系统》（CSAPP）这门课的助教时，我已经自学一年多了。这一年多来我无比享受这种自学模式，为了分享这种快乐，我为自己的小班同学做过一个 [CS自学资料整理仓库](https://github.com/PKUFlyingPig/Self-learning-Computer-Science)。当时纯粹是心血来潮，因为我也不敢公然鼓励大家翘课自学。
+在我2020年秋季学期担任《深入理解计算机系统》（CSAPP）这门课的助教时，我已经自学一年多了。这一年多来我无比享受这种自学模式，为了分享这种快乐，我为自己的研讨班学生做过一个 [CS自学资料整理仓库](https://github.com/PKUFlyingPig/Self-learning-Computer-Science)。当时纯粹是心血来潮，因为我也不敢公然鼓励大家翘课自学。
 
 但随着又一年时间的维护，这个仓库的内容已经相当丰富，基本覆盖了计科、智能系、软工系的绝大多数课程，我也为每个课程都建了各自的 GitHub 仓库，汇总我用到的自学资料以及作业实现。
 
 直到大四开始凑学分毕业的时候，我打开自己的培养方案，我发现它已经是我这个自学仓库的子集了，而这距离我开始自学也才两年半而已。于是，一个大胆的想法在我脑海中浮现：也许，我可以打造一个自学式的培养方案，把我这三年自学经历中遇到的坑、走过的路记录下来，以期能为后来的学弟学妹们贡献自己的一份微薄之力。
 
-如果大家可以在三年不到的时间里就能建立起整座CS的基础大厦，能有相对扎实的数学功底和代码能力，经历过数十个千行代码量的 Project 的洗礼，掌握至少 C/C++/Java/JS/Python/Go/Rust 等主流语言，对算法、电路、体系、网络、操统、编译、人工智能、机器学习、计算机视觉、自然语言处理、强化学习、密码学、信息论、博弈论、数值分析、统计学、分布式、数据库、图形学、Web开发、云服务、超算等等方面均有涉猎。我想，你将有足够的底气和自信选择自己感兴趣的方向，无论是就业还是科研，你都将有相当的竞争力。
+如果大家可以在三年不到的时间里就能建立起整座 CS 的基础大厦，能有相对扎实的数学功底和代码能力，经历过数十个千行代码量的 Project 的洗礼，掌握至少 C/C++/Java/JS/Python/Go/Rust 等主流语言，对算法、电路、体系、网络、操统、编译、人工智能、机器学习、计算机视觉、自然语言处理、强化学习、密码学、信息论、博弈论、数值分析、统计学、分布式、数据库、图形学、Web开发、云服务、超算等等方面均有涉猎。我想，你将有足够的底气和自信选择自己感兴趣的方向，无论是就业还是科研，你都将有相当的竞争力。
 
 因为我坚信，既然你能坚持听我 BB 到这里，你一定不缺学好 CS 的能力，你只是没有一个好的老师，给你讲一门好的课程。而我，将力图根据我三年的体验，为你挑选这样的课程。
 
@@ -59,7 +60,7 @@
 
 自学的另一大好处就是博采众长。计算机系的几大核心课程：体系、网络、操统、编译，每一门我基本都上过不同大学的课程，不同的教材、不同的知识点侧重、不同的 project 将会极大丰富你的视野，也会让你理解错误的一些内容得到及时纠正。
 
-自学的第三个好处是时间自由，具体原因省略。
+自学的第三个好处是时间自由。大学的课余时间本就相对自由，再加上不用去上课的话更是可以放飞自我地安排自学时间和进度。我大二的时候赶上疫情在家窝了大半年，返校之后也基本没有线下去过教室上课，对绩点也毫无影响。
 
 ## 自学的坏处
 
@@ -67,9 +68,9 @@
 
 第一就是交流沟通的不便。我其实是一个很热衷于提问的人，对于所有没有弄明白的点，我都喜欢穷追到底。但当你面对着屏幕听到老师讲了一个你没明白的知识点的时候，你无法顺着网线到另一端向老师问个明白。我努力通过独立思考和善用 Google 来缓解这一点，但是，如果能有几个志同道合的伙伴结伴自学，那将是极好的。关于交流群的建立，大家可以参考仓库 `README` 中的教程。
 
-第二就是这些自学的课程基本都是英文的。从视频到slides到作业全是英文，所以有一定的门槛。不过我觉得这个挑战如果你克服了的话对你是极为有利的。因为在当下，虽然我很不情愿，但也不得不承认，在计算机领域，很多优质的文档、论坛、网站都是全英文的。养成英文阅读的习惯，在赤旗插遍世界之前，还是有一定好处的（狗头保命）。
+第二就是这些自学的课程基本都是英文的。从视频到课件再到作业全是英文，所以有一定的门槛。我尽量在汇总课程视频资源的时候寻找带中文字幕的搬运视频，但大多数课程还是只有机翻或者生肉，而课件和作业肯定都是英文的。不过我觉得这是个值得努力克服的挑战，因为在当下，虽然我很不情愿，但也不得不承认，在计算机领域，很多优质的文档、论坛、网站都是英文居多。养成英文阅读的习惯，在赤旗插遍世界之前，还是有一定好处的（狗头保命）。
 
-第三，也是我觉得最困难的一点，就是自律。因为没有 DDL 有时候真的是一件可怕的事情，特别是随着学习的深入，国外的很多课程是相当虐的。你得有足够的驱动力强迫自己静下心来，阅读几十页的 Project Handout，理解上千行的代码框架，忍受数个小时的 debug 时光。而这一切，没有学分，没有绩点，没有老师，没有同学，只有一个信念 —— 你在变强。
+第三，也是我觉得最困难的一点，就是自律。因为没有 DDL 有时候真的是一件可怕的事情。特别是随着学习的深入，国外的很多课程是相当虐的。你得有足够的驱动力强迫自己静下心来，阅读几十页的 Project Handout，理解上千行的代码框架，忍受数个小时的 debug 时光。而这一切，没有学分，没有绩点，没有老师，没有同学，只有一个信念 —— 你在变强。
 
 ## 这本书适合谁
 
@@ -79,7 +80,7 @@
 
 ## 特别鸣谢
 
-在这里，我怀着崇敬之心真诚地感谢所有将课程资源无偿开源的各位教授们。这些课程倾注了他们数十年教学生涯的积淀和心血，他们却选择无私地让所有人享受到如此高质量的CS教育。没有他们，我的大学生活不会这样充实而快乐。很多教授在我给他们发了感谢邮件之后，甚至会回复上百字的长文，真的让我无比感动。他们也时刻激励着我，做一件事，就得用心做好，无论是科研，还是为人。
+在这里，我怀着崇敬之心真诚地感谢所有将课程资源无偿开源的各位教授们。这些课程倾注了他们数十年教学生涯的积淀和心血，他们却选择无私地让所有人享受到如此高质量的 CS 教育。没有他们，我的大学生活不会这样充实而快乐。很多教授在我给他们发了感谢邮件之后，甚至会回复上百字的长文，真的让我无比感动。他们也时刻激励着我，做一件事，就得用心做好，无论是学习、科研、还是为人。
 
 ## 你也想加入到贡献者的行列
 
@@ -87,4 +88,12 @@
 
 ## 关于交流群的建立
 
-方法参见仓库的 `README.md`。
+本书支持页面评论功能，因此如果你想自学某课程，可以自己建立群聊后（QQ 微信皆可）在对应的课程页面下方发表评论，注明你的学习目标以及加入交流群的途径。此外，过去已有不少朋友在 issue 里建立了类似群聊，可以自行选择直接加入。
+
+## 请作者喝杯下午茶
+
+本书的内容是完全开源免费的，如果你觉得该项目对你真的有帮助，可以给仓库点个 star 或者请作者喝一杯下午茶。
+
+<figure markdown>
+  ![Image title](./images/sponsor.png){ width="500" }
+</figure>

docs/人工智能/CS188.en.md

@@ -0,0 +1,20 @@
+# CS188: Introduction to Artificial Intelligence
+
+## Course Overview
+
+- University：UC Berkeley
+- Prerequisites：CS70
+- Programming Language：Python
+- Course Difficulty：🌟🌟🌟
+- Estimated Hours：50 hours
+
+This introductory artificial intelligence course at UC Berkeley provides in-depth and accessible course notes, making it possible to grasp the material without necessarily watching the lecture videos. The course follows the chapters of the classic AI textbook *Artificial Intelligence: A Modern Approach*, covering topics such as search pruning, constraint satisfaction problems, Markov decision processes, reinforcement learning, Bayesian networks, Hidden Markov Models, as well as fundamental concepts in machine learning and neural networks.
+
+The Fall 2018 version of the course offered free access to gradescope, allowing students to complete written assignments online and receive real-time assessment results. The course also includes 6 projects of high quality, featuring the recreation of the classic Pac-Man game. These projects challenge students to apply their AI knowledge to implement various algorithms, enabling their Pac-Man to navigate mazes, evade ghosts, and collect pellets.
+
+## Course Resources
+
+- Course Websites：[Fall 2022](https://inst.eecs.berkeley.edu/~cs188/fa22/), [Fall 2018](https://inst.eecs.berkeley.edu/~cs188/fa18/index.html)
+- Course Videos：[Fall 2022](https://inst.eecs.berkeley.edu/~cs188/fa22/), [Fall 2018](https://inst.eecs.berkeley.edu/~cs188/fa18/index.html), with links to each lecture on the course website
+- Course Textbook：Artificial intelligence: A Modern Approach
+- Course Assignments：Online assessments for written assignments and projects, details available on the course website

docs/人工智能/CS188.md

@@ -10,11 +10,11 @@
 
 伯克利的人工智能入门课，课程 notes 写得非常深入浅出，基本不需要观看课程视频。课程内容的安排基本按照人工智能的经典教材 *Artificial intelligence: A Modern Approach* 的章节顺序，覆盖了搜索剪枝、约束满足问题、马尔可夫决策过程、强化学习、贝叶斯网络、隐马尔可夫模型以及基础的机器学习和神经网络的相关内容。
 
-2018年秋季学期的版本免费开放了 gradescope，大家可以在线完成书面作业并实时得到测评结果。同时课程的 6 个 Project 也是质量爆炸，复现了经典的 Packman（吃豆人）小游戏，会让你利用学到的 AI 知识，去实现相关算法，让你的吃豆人在迷宫里自由穿梭，躲避鬼怪，收集豆子。
+目前Spring 2024是最新一期视频与资料完整、开放了旁听gradescope的版本，大家可以在线完成书面作业并实时得到测评结果。同时课程的 6 个 Project 也是质量爆炸，复现了经典的 Packman（吃豆人）小游戏，会让你利用学到的 AI 知识，去实现相关算法，让你的吃豆人在迷宫里自由穿梭，躲避鬼怪，收集豆子。
 
 ## 课程资源
 
-- 课程网站：[Fall 2022](https://inst.eecs.berkeley.edu/~cs188/fa22/)，[Fall 2018](https://inst.eecs.berkeley.edu/~cs188/fa18/index.html)
-- 课程视频：[Fall 2022](https://inst.eecs.berkeley.edu/~cs188/fa22/)，[Fall 2018](https://inst.eecs.berkeley.edu/~cs188/fa18/index.html)，每节课的链接详见课程网站
+- 课程网站：[Spring 2024](https://inst.eecs.berkeley.edu/~cs188/sp24/)
+- 课程视频：每节课的链接详见课程网站
 - 课程教材：Artificial intelligence: A Modern Approach
 - 课程作业：在线测评书面作业和 Projects，详见课程网站

docs/人工智能/CS50.en.md

@@ -12,10 +12,10 @@ A very basic introductory AI course, what makes it stand out is the 12 well-desi
 
 ## Course Resources
 
-- Course Website: <https://cs50.harvard.edu/ai/2020/>
-- Recordings: <https://cs50.harvard.edu/ai/2020/>
+- Course Website: [2024](https://cs50.harvard.edu/ai/2024/)、[2020](https://cs50.harvard.edu/ai/2020/)
+- Recordings: [2024](https://cs50.harvard.edu/ai/2024/)、[2020](https://cs50.harvard.edu/ai/2020/)
 - Textbooks: No textbook is needed in this course.
-- Assignments: <https://cs50.harvard.edu/ai/2020/> with 12 programming labs of high quality mentioned above.
+- Assignments: [2024](https://cs50.harvard.edu/ai/2024/)、[2020](https://cs50.harvard.edu/ai/2020/) with 12 programming labs of high quality mentioned above.
 
 ## Personal Resources
 

docs/人工智能/CS50.md

@@ -12,10 +12,10 @@
 
 ## 课程资源
 
-- 课程网站：<https://cs50.harvard.edu/ai/2020/>
-- 课程视频：<https://cs50.harvard.edu/ai/2020/>
+- 课程网站：[2024](https://cs50.harvard.edu/ai/2024/)、[2020](https://cs50.harvard.edu/ai/2020/)
+- 课程视频：[2024](https://cs50.harvard.edu/ai/2024/)、[2020](https://cs50.harvard.edu/ai/2020/)
 - 课程教材：无
-- 课程作业：<https://cs50.harvard.edu/ai/2020/>，12个精巧的编程作业
+- 课程作业：[2024](https://cs50.harvard.edu/ai/2024/)、[2020](https://cs50.harvard.edu/ai/2020/)，12个精巧的编程作业
 
 ## 资源汇总
 

docs/人工智能/Neural Networks：Zero to Hero.en.md

@@ -0,0 +1,24 @@
+# Neural Networks: Zero to Hero  
+
+## Description  
+
+- **Instructor:** Andrej Karpathy  
+- **Prerequisites:** Basic Python programming and some familiarity with deep learning concepts  
+- **Programming Language:** Python  
+- **Difficulty:** 🌟🌟🌟🌟  
+- **Class Hours:** Approximately 19 hours  
+
+This hands-on deep learning course, taught by Andrej Karpathy, provides a detailed and intuitive introduction to neural networks and their underlying principles. The course starts with foundational concepts such as backpropagation and micrograd before progressing to building language models, WaveNets, and GPT from scratch. The emphasis is on practical implementation, with step-by-step coding explanations to help students understand and build complex models from the ground up.  
+
+## Instructor Information  
+
+Andrej Karpathy is a renowned AI researcher and educator with extensive experience in deep learning and neural networks. He was the **Senior Director of AI at Tesla**, leading the **computer vision team for Tesla Autopilot** from 2017 to 2022. Prior to that, he was a **research scientist and founding member at OpenAI** (2015-2017). In 2023, he returned to OpenAI, contributing to improvements in GPT-4 for ChatGPT. In 2024, he founded **Eureka Labs**, an AI+Education company.  
+
+Karpathy holds a **PhD from Stanford University**, where he worked on convolutional and recurrent neural networks with **Fei-Fei Li**. He has collaborated with leading AI researchers, including **Daphne Koller, Andrew Ng, Sebastian Thrun, and Vladlen Koltun**. He also taught the first deep learning course at Stanford, **CS 231n: Convolutional Neural Networks for Visual Recognition**, which became one of the largest classes at the university.  
+
+## Course Resources  
+
+- **Lecture Videos:** [YouTube Playlist](https://www.youtube.com/watch?v=VMj-3S1tku0&list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ)  
+- **Assignments:** Self-guided projects and code implementation exercises available throughout the lectures  
+
+For more information, watch the full playlist on YouTube.  

docs/人工智能/Neural Networks：Zero to Hero.md

@@ -0,0 +1,24 @@
+# 神经网络：从零到英雄  
+
+## 课程简介  
+
+- **讲师：** Andrej Karpathy  
+- **先修要求：** 具备基本的 Python 编程能力，并对深度学习概念有所了解  
+- **编程语言：** Python  
+- **难度：** 🌟🌟🌟🌟  
+- **课程时长：** 约 19 小时  
+
+本课程由 Andrej Karpathy 讲授，是一个深入浅出的深度学习课程，旨在帮助学习者掌握神经网络的核心原理。课程从基础概念（如反向传播和 micrograd）入手，逐步带领学员构建语言模型、WaveNet，并从零开始实现 GPT。课程以实践为主，提供逐步讲解的代码示例，让学员能够理解并构建复杂的神经网络模型。  
+
+## 讲师信息  
+
+Andrej Karpathy 是一位知名的人工智能研究员和教育者，在深度学习和神经网络领域具有丰富的经验。他曾在 **2017 至 2022 年担任特斯拉 AI 部门高级总监**，领导 **Tesla Autopilot 计算机视觉团队**，负责数据标注、神经网络训练、部署等工作。在此之前，他曾是 **OpenAI 的研究科学家和创始成员**（2015-2017）。2023 年，他回归 OpenAI，参与改进 ChatGPT 的 GPT-4。2024 年，他创立了 **Eureka Labs**，一家专注于 AI + 教育的公司。  
+
+Karpathy 拥有 **斯坦福大学博士学位**，师从 **Fei-Fei Li（李飞飞）**，主要研究卷积神经网络和循环神经网络及其在计算机视觉和自然语言处理中的应用。他曾与 **Daphne Koller、Andrew Ng（吴恩达）、Sebastian Thrun 和 Vladlen Koltun** 等知名研究员合作。此外，他还在斯坦福大学教授了首个深度学习课程 **CS 231n: 卷积神经网络与视觉识别**，该课程逐渐发展为斯坦福大学规模最大的课程之一。  
+
+## 课程资源  
+
+- **课程视频：** [YouTube 播放列表](https://www.youtube.com/watch?v=VMj-3S1tku0&list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ)  
+- **作业：** 课程中提供的代码实践和项目练习  
+
+更多信息请访问 YouTube 观看完整课程视频。  

docs/体系结构/CA.en.md

@@ -0,0 +1,26 @@
+# ETH: Computer Architecture
+
+## Course Overview
+
+- University: ETH Zurich
+- Prerequisites: [DDCA](https://csdiy.wiki/%E4%BD%93%E7%B3%BB%E7%BB%93%E6%9E%84/DDCA/)
+- Programming Language: C/C++, Verilog
+- Difficulty Level: 🌟🌟🌟🌟
+- Estimated Study Time: 70+ hours
+
+This course, taught by Professor Onur Mutlu, delves into computer architecture. It appears to be an advanced course following [DDCA](https://csdiy.wiki/%E4%BD%93%E7%B3%BB%E7%BB%93%E6%9E%84/DDCA/), aimed at teaching how to design control and data paths hardware for a MIPS-like processor, how to execute machine instructions concurrently through pipelining and simple superscalar execution, and how to design fast memory and storage systems. According to student feedback, the course is at least more challenging than CS61C, and some of its content is cutting-edge. Bilibili uploaders recommend it as a supplement to Carnegie Mellon University's 18-447 course. The reading materials provided are extensive, akin to attending a semester's worth of lectures.
+
+The official website description is as follows:
+> "We will learn the fundamental concepts of the different parts of modern computing systems, as well as the latest major research topics in Industry and Academia. We will extensively cover memory systems (including DRAM and new Non-Volatile Memory technologies, memory controllers, flash memory), new paradigms like processing-in-memory, parallel computing systems (including multicore processors, coherence and consistency, GPUs), heterogeneous computing, interconnection networks, specialized systems for major data-intensive workloads (e.g., graph analytics, bioinformatics, machine learning), etc. We will focus on fundamentals as well as cutting-edge research. Significant attention will be given to real-life examples and tradeoffs, as well as critical analysis of modern computing systems."
+
+The programming practice involves using Verilog to design and simulate RT implementations of a MIPS-like pipeline processor to enhance theoretical course understanding. The initial experiments include Verilog CPU pipeline programming. Additionally, students will develop a cycle-accurate processor simulator in C and explore processor design options using this simulator.
+
+## Course Resources
+
+- Course Website: [2020 Fall](https://safari.ethz.ch/architecture/fall2022/doku.php?id=start), [2022 Fall](https://safari.ethz.ch/architecture/fall2022/doku.php?id=start)
+- Course Videos: Official videos available on the course website. A [2020 version is available on Bilibili](https://www.bilibili.com/video/BV1Vf4y1i7YG/?vd_source=77d47fcb2bac41ab4ad02f265b3273cf).
+- Course Textbooks: No designated textbook; each lecture has an extensive bibliography for reading.
+- Course Assignments: 5 Projects, mostly related to memory and cache, detailed on the [lab page of the course website](https://safari.ethz.ch/architecture/fall2022/doku.php?id=labs).
+
+## Resource Summary
+Some universities in China have introduced this course, so interested students can find additional resources through online searches.

docs/体系结构/CA.md

@@ -0,0 +1,29 @@
+# ETH: Computer Architecture
+
+## 课程简介
+
+- 所属大学：ETH Zurich
+- 先修要求：[DDCA](https://csdiy.wiki/%E4%BD%93%E7%B3%BB%E7%BB%93%E6%9E%84/DDCA/)
+- 编程语言：C/C++，verilog
+- 课程难度：🌟🌟🌟🌟
+- 预计学时：70 小时 +
+
+讲解计算机体系结构，授课教师是 Onur Mutlu 教授。本课程根据课程描述应该是[DDCA](https://csdiy.wiki/%E4%BD%93%E7%B3%BB%E7%BB%93%E6%9E%84/DDCA/)的进阶课程，课程目标是学习如何为类MIPS处理器设计控制和数据通路硬件，如何通过流水线和简单的超标量执行使机器指令同时执行，以及如何设计快速的内存和存储系统。根据同学反馈，从课程本身的难度上说，至少高于 CS61C ，课程的部分内容十分前沿，B站搬运UP主建议大家作为卡内基梅隆大学18-447的补充。所提供的阅读材料十分丰富，相当于听了一学期讲座。
+
+以下是官网的介绍：
+>We will learn the fundamental concepts of the different parts of modern computing systems, as well as the latest major research topics in Industry and Academia. We will extensively cover memory systems (including DRAM and new Non-Volatile Memory technologies, memory controllers, flash memory), new paradigms like processing-in-memory, parallel computing systems (including multicore processors, coherence and consistency, GPUs), heterogeneous computing, interconnection networks, specialized systems for major data-intensive workloads (e.g. graph analytics, bioinformatics, machine learning), etc. We will focus on fundamentals as well as cutting-edge research. Significant attention will be given to real-life examples and tradeoffs, as well as critical analysis of modern computing systems.
+
+编程实践采取 Verilog 设计和模拟类 MIPS 流水线处理器的寄存器传输（RT）实现，以此加强对理论课程的理解。因此前几个实验会有 verilog 的 CPU 流水线编程。同时还将使用C语言开发一个周期精确的处理器模拟器，并使用该模拟器探索处理器设计选项。
+
+
+## 课程资源
+
+- 课程网站：[2020 Fall](https://safari.ethz.ch/architecture/fall2022/doku.php?id=start), [2022 Fall](https://safari.ethz.ch/architecture/fall2022/doku.php?id=start)
+- 课程视频：官方视频详见课程网站。B站有个[2020年版本搬运](https://www.bilibili.com/video/BV1Vf4y1i7YG/?vd_source=77d47fcb2bac41ab4ad02f265b3273cf)。
+- 课程教材：无指定教材，每个 lecture 都有大量文献可供阅读
+- 课程作业：5 个 Project ，大多与内存和cache相关，具体内容见[课程网站的lab界面](https://safari.ethz.ch/architecture/fall2022/doku.php?id=labs)
+
+## 资源汇总
+国内有高校引入了这门课，因此有需要的同学可以搜索到一些资源。
+
+

docs/体系结构/CS61C.en.md

@@ -16,11 +16,15 @@ In a word, this is the best computer architecture course I have ever taken.
 
 ## Course Resources
 
-- Course Website: <https://cs61c.org/su20/>
-- Recordings: [Youtube](https://www.youtube.com/playlist?list=PLDoI-XvXO0aqgoMQvogzmf7CKiSMSUS3M)
-- Textbook: None
-- Assignments: 11 Labs, 4 Projects, the course website has specific requirements.
+- [Course Website](https://cs61c.org/)
+- Course Website (Backup): [Fa24-WayBack Machine](https://web.archive.org/web/20241219154359/https://cs61c.org/fa24/), [Fa20-WayBack Machine](https://web.archive.org/web/20220120134001/https://inst.eecs.berkeley.edu/~cs61c/fa20/), [Fa20-Backup](https://www.learncs.site/docs/curriculum-resource/cs61c/syllabus)
+- Recordings: [Su20-Bilibili](https://www.bilibili.com/video/BV1fC4y147iZ/?share_source=copy_web&vd_source=7c3823b46a52fbbef42b79e01d55c300), [Su20-Youtube](https://youtube.com/playlist?list=PLDoI-XvXO0aqgoMQvogzmf7CKiSMSUS3M&si=62aaH5a_PMGrAT2Y), [Fa20-Bilibili](https://www.bilibili.com/video/BV17b42177VG/?share_source=copy_web&vd_source=7c3823b46a52fbbef42b79e01d55c300), [Fa20-Youtube](https://youtube.com/playlist?list=PL0j-r-omG7i0-mnsxN5T4UcVS1Di0isqf&si=CG1EjQiPcw7r7Vs4)
+- Assignments: [Fa20-Backup](https://github.com/InsideEmpire/CS61C-Assignment#)
 
 ## Personal Resources
 
 All the resources and assignments used by @PKUFlyingPig in this course are maintained in [PKUFlyingPig/CS61C-summer20 - GitHub](https://github.com/PKUFlyingPig/CS61C-summer20).
+
+All the resources and assignments used by @InsideEmpire in this course are maintained in [@InsideEmpire/CS61C-fall20 - GitHub](https://github.com/InsideEmpire/CS61C-PathwayToSuccess).
+
+All the resources and assignments used by @RisingUppercut in this course are maintained in [@RisingUppercut/CS61C-fall24 - GitHub](https://github.com/RisingUppercut/CS61C_2024_Fall).

docs/体系结构/CS61C.md

@@ -19,11 +19,15 @@
 
 ## 课程资源
 
-- 课程网站：<https://cs61c.org/su20/>
-- 课程视频：[B 站](https://www.bilibili.com/video/BV1fC4y147iZ), [Youtube](https://www.youtube.com/playlist?list=PLDoI-XvXO0aqgoMQvogzmf7CKiSMSUS3M)
-- 课程教材：无
-- 课程作业：11 个 Lab，4 个 Project，具体要求详见课程网站
+- [课程网站](https://cs61c.org/)
+- 课程网站 (页面备份): [Fa24-WayBack Machine](https://web.archive.org/web/20241219154359/https://cs61c.org/fa24/), [Fa20-WayBack Machine](https://web.archive.org/web/20220120134001/https://inst.eecs.berkeley.edu/~cs61c/fa20/), [Fa20-备份](https://www.learncs.site/docs/curriculum-resource/cs61c/syllabus)
+- 课程视频: [Su20-Bilibili](https://www.bilibili.com/video/BV1fC4y147iZ/?share_source=copy_web&vd_source=7c3823b46a52fbbef42b79e01d55c300), [Su20-Youtube](https://youtube.com/playlist?list=PLDoI-XvXO0aqgoMQvogzmf7CKiSMSUS3M&si=62aaH5a_PMGrAT2Y), [Fa20-Bilibili](https://www.bilibili.com/video/BV17b42177VG/?share_source=copy_web&vd_source=7c3823b46a52fbbef42b79e01d55c300), [Fa20-Youtube](https://youtube.com/playlist?list=PL0j-r-omG7i0-mnsxN5T4UcVS1Di0isqf&si=CG1EjQiPcw7r7Vs4)
+- 课程作业: [Fa20-备份](https://github.com/InsideEmpire/CS61C-Assignment#)
 
 ## 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/CS61C-summer20 - GitHub](https://github.com/PKUFlyingPig/CS61C-summer20) 中。
+
+@InsideEmpire 在学习这门课中用到的所有资源和作业实现都汇总在 [@InsideEmpire/CS61C-fall20 - GitHub](https://github.com/InsideEmpire/CS61C-PathwayToSuccess) 中。
+
+@RisingUppercut 在学习这门课中用到的所有资源和作业实现都汇总在 [@RisingUppercut/CS61C-fall24 - GitHub](https://github.com/RisingUppercut/CS61C_2024_Fall) 中。

docs/体系结构/DDCA.md

@@ -1,4 +1,4 @@
-# Digital Design and Computer Architecture
+# ETH Zurich：Digital Design and Computer Architecture
 
 ## 课程简介
 
@@ -14,8 +14,9 @@
 
 ## 课程资源
 
-- 课程网站：<https://safari.ethz.ch/digitaltechnik/spring2020/>
-- 课程视频：<https://www.youtube.com/playlist?list=PL5Q2soXY2Zi_FRrloMa2fUYWPGiZUBQo2>
+- 课程网站：[2020](https://safari.ethz.ch/digitaltechnik/spring2020/),[2023](https://safari.ethz.ch/digitaltechnik/spring2023/)
+- 课程视频：[youtube](https://www.youtube.com/playlist?list=PL5Q2soXY2Zi_FRrloMa2fUYWPGiZUBQo2), [B站2020年版本搬运](https://www.bilibili.com/video/BV1MA411s7qq/?vd_source=77d47fcb2bac41ab4ad02f265b3273cf)
 - 课程教材1：Patt and Patel, Introduction to Computing Systems
 - 课程教材2：Harris and Harris, Digital Design and Computer Architecture (MIPS Edition)
+中文译本为《数字设计和计算机体系结构(原书第2版)》
 - 课程实验：9 个实验从零开始设计 MIPS CPU，详见课程网站

docs/体系结构/N2T.en.md

@@ -28,7 +28,7 @@ This course extracts the essence of computers while omitting the tedious and com
 
 - Course Website: [Nand2Tetris I](https://www.coursera.org/learn/build-a-computer/home/week/1), [Nand2Tetris II](https://www.coursera.org/learn/nand2tetris2/home/welcome)
 - Recordings: Refer to course website
-- Textbook: [The Elements of Computing Systems: Building a Modern Computer from First Principles (CN-zh version)](book)
+- Textbook: [The Elements of Computing Systems: Building a Modern Computer from First Principles (CN-zh version)][book]
 - Assignments: 10 projects to construct a computer, refer to the course website for more details 
 
 [book]: https://github.com/PKUFlyingPig/NandToTetris/blob/master/%5B%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%B3%BB%E7%BB%9F%E8%A6%81%E7%B4%A0%EF%BC%9A%E4%BB%8E%E9%9B%B6%E5%BC%80%E5%A7%8B%E6%9E%84%E5%BB%BA%E7%8E%B0%E4%BB%A3%E8%AE%A1%E7%AE%97%E6%9C%BA%5D.(%E5%B0%BC%E8%90%A8).%E5%91%A8%E7%BB%B4.%E6%89%AB%E6%8F%8F%E7%89%88.pdf

docs/使用指南.en.md

@@ -0,0 +1,47 @@
+# **How to Use This Book**
+
+As the number of contributors grows, the content of this book keeps expanding. It is impractical and unnecessary to try to complete all the courses in the book. Attempting to do so might even be counterproductive, resulting in effort without reward. To better align with our readers and make this book truly useful for you, I have roughly divided readers into the following three categories based on their needs. Everyone can plan their own self-study program accurately according to their actual situation.
+
+## **Freshmen**
+
+If you have just entered the university or are in the lower grades, and you are studying or planning to switch to computer science, then you are lucky. As studying is your main task, you have ample time and freedom to learn what you are interested in without the pressure of work and daily life. You needn't be overly concerned with utilitarian thoughts like "is it useful" or "can it help me find a job". So, how should you arrange your studies? The first point is to break away from the passive learning style formed in high school. As a small-town problem solver, I know that most Chinese high schools fill every minute of your day with tasks, and you just need to passively follow the schedule. As long as you are diligent, the results won’t be too bad. However, once you enter university, you have much more freedom. All your extracurricular time is yours to use, and no one will organize knowledge points or summarize outlines for you. Exams are not as formulaic as in high school. If you still hold the mentality of a "good high school student", following everything step by step, the results may not be as expected. The professional training plan may not be reasonable, the teaching may not be responsible, attending classes may not guarantee understanding, and even the exam content may not relate to what was taught. Jokingly, you might feel that the whole world is against you, and you can only rely on yourself.
+
+Given this reality, if you want to change it, you must first survive and have the ability to question it. In the lower grades, it’s important to lay a solid foundation. This foundation is comprehensive, covering both in-class knowledge and practical skills, which are often lacking in China's undergraduate computer science education. Based on personal experience, I offer the following suggestions for your reference.
+
+First, learn how to write "elegant" code. Many programming introductory courses in China can be extremely boring syntax classes, less effective than reading official documentation. Initially, letting students understand what makes code elegant and what constitutes "bad taste" is beneficial. Introductory courses usually start with procedural programming (like C language), but even here, the concepts of **modularity** and **encapsulation** are crucial. If you write code just to pass on OpenJudge, using lengthy copy-pasting and bloated main functions, your code quality will remain poor. For larger projects, endless debugging and maintenance costs will overwhelm you. So, constantly ask yourself, is there a lot of repetitive code? Is the current function too complex (Linux advocates each function should do only one thing)? Can this code be abstracted into a function? Initially, this may seem cumbersome for simple problems, but remember, good habits are invaluable. Even middle school students can master C language, so why should a company hire you as a software engineer?
+
+After procedural programming, the second semester of the freshman year usually introduces object-oriented programming (like C++ or Java). I highly recommend [MIT 6.031: Software Construction](软件工程/6031.md) course notes, which use Java (switch to TypeScript after 2022) to explain how to write “elegant” code in detail, including Test-Driven development, function Specification design, exception handling, and more. Also, understanding common design patterns is necessary when learning object-oriented programming. Domestic object-oriented courses can easily become dull syntax classes, focusing on inheritance syntax and puzzling questions, neglecting that these are rarely used in real-world development. The essence of object-oriented programming is teaching students to abstract real problems into classes and their relationships, and design patterns are the essence of these abstractions. I recommend the book ["Big Talk Design Patterns"](https://book.douban.com/subject/2334288/), which is very easy to understand.
+
+Second, try to learn some productivity-enhancing tools and skills, such as Git, Shell, Vim. I strongly recommend the [MIT missing semester](编程入门/MIT-Missing-Semester.md) course. Initially, you may feel awkward, but force yourself to use them, and your development efficiency will skyrocket. Additionally, many applications can greatly increase your productivity. A rule of thumb is: any action that requires your hands to leave the keyboard should be eliminated. For example, switching applications, opening files, browsing the web - there are plugins for these (like [Alfred](https://www.alfredapp.com/) for Mac). If you find an daily operation that takes more than 1 second, try to reduce it to 0.1 seconds. After all, you'll be dealing with computers for decades, so forming a smooth workflow can greatly enhance efficiency. Lastly, learn to touch type! If you still need to look at the keyboard while typing, find a tutorial online and learn to type without looking. This will significantly increase your development efficiency.
+
+Third, balance coursework and self-learning. We feel angry about the institution but must also follow the rules, as GPA is still important for postgraduate recommendations. Therefore, in the first year, I suggest focusing on the curriculum, complemented by high-quality extracurricular resources. For example, for calculus and linear algebra, refer to [MIT 18.01/18.02](./数学基础/MITmaths.md) and [MIT 18.06](./数学基础/MITLA.md). During holidays, learn Python through [UCB CS61A](./编程入门/Python/CS61A.md). Also, focus on good programming habits and practical skills mentioned above. From my experience, mathematics courses matter a lot for your GPA in the first year, and the content of math exams varies greatly between different schools and teachers. Self-learning might help you understand the essence of mathematics, but it may not guarantee good grades. Therefore, it’s better to specifically practice past exams. 
+
+In your sophomore year, as computer science courses become the majority, you can fully immerse yourself in self-learning. Refer to [A Reference Guide for CS Learning](./CS学习规划.md), a guide I created based on three years of self-learning, introducing each course and its importance. For every course in your curriculum, this guide should have a corresponding one, and I believe they are of higher quality. If there are course projects, try to adapt labs or projects from these self-learning courses. For example, I took an operating systems course and found the teacher was still using experiments long abandoned by UC Berkeley, so I emailed the teacher to switch to the [MIT 6.S081](./操作系统/MIT6.S081.md) xv6 Project I was studying. This allowed me to self-learn while inadvertently promoting curriculum reform. In short, be flexible. Your goal is to master knowledge in the most convenient and efficient way. Anything that contradicts this goal can be “fudged” as necessary. With this attitude, after my junior year, I barely attended offline classes (I spent most of my sophomore year at home due to the pandemic), and it had no impact on my GPA.
+
+Finally, I hope everyone can be less impetuous and more patient in their pursuit. Many ask if self-learning requires strong self-discipline. It depends on what you want. If you still hold the illusion that mastering a programming language will earn you a high salary and a share of the internet’s profits, then whatever I say is pointless. Initially, my motivation was out of pure curiosity and a natural desire for knowledge, not for utilitarian reasons. The process didn't involve “extraordinary efforts”; I spent my days in college as usual and gradually accumulated this wealth of materials. Now, as the US-China confrontation becomes a trend, we still humbly learn techniques from the West. Who will change this? You, the newcomers. So, go for it, young man!
+
+## **Simplify the Complex**
+
+If you have graduated and started postgraduate studies, or have begun working, or are in another field and want to learn coding in your spare time, you may not have enough time to systematically complete the materials in [A Reference Guide for CS Learning](./CS学习规划.md), but still want to fill the gaps in your undergraduate foundation. Considering that these readers usually has some programming experience, there is no need to repeat introductory courses. From a practical standpoint, since the general direction of work is already determined, there is no need to deeply study every branch of computer science. Instead, focus on general principles and skills. Based on my own experience, I've selected the most important and highest quality core professional courses to deepen readers' understanding of computer science. After completing these courses, regardless of your specific job, I believe you won't just be an ordinary coder, but will have a deeper understanding of the underlying logic of computers.
+
+| Course Direction    | Course Name                                          |
+|---------------------|------------------------------------------------------|
+| Discrete Mathematics and Probability Theory | [UCB CS70: Discrete Math and Probability Theory](数学进阶/CS70.md) |
+| Data Structures and Algorithms | [Coursera: Algorithms I & II](数据结构与算法/Algo.md) |
+| Software Engineering | [MIT 6.031: Software Construction](软件工程/6031.md) |
+| Full-Stack Development | [MIT Web Development Course](Web开发/mitweb.md) |
+| Introduction to Computer Systems | [CMU CS15213: CSAPP](计算机系统基础/CSAPP.md) |
+| Introductory System Architecture | [Coursera: Nand2Tetris](体系结构/N2T.md) |
+| Advanced System Architecture | [CS61C: Great Ideas in Computer Architecture](体系结构/CS61C.md) |
+| Principles of Databases | [CMU 15-445: Introduction to Database Systems](数据库系统/15445.md) |
+| Computer Networking | [Computer Networking: A Top-Down Approach](计算机网络/topdown.md) |
+| Artificial Intelligence | [Harvard CS50: Introduction to AI with Python](人工智能/CS50.md) |
+| Deep Learning | [Coursera: Deep Learning](深度学习/CS230.md) |
+
+## **Focused and Specialized**
+
+If you have a solid grasp of the core professional courses in computer science and have already determined your work or research direction, then there are many courses in the book not mentioned in [A Reference Guide for CS Learning](./CS学习规划.md) for you to explore.
+
+As the number of contributors increases, new branches such as **Advanced Machine Learning** and **Machine Learning Systems** will be added to the navigation bar. Under each branch, there are several similar courses from different schools with different emphases and experiments, such as the **Operating Systems** branch, which includes courses from MIT, UC Berkeley, Nanjing University, and Harbin Institute of Technology. If you want to delve into a field, studying these similar courses will give you different perspectives on similar knowledge. Additionally, I plan to contact researchers in related fields to share research learning paths in specific subfields, enhancing the depth of the CS Self-learning Guide while pursuing breadth.
+
+If you want to contribute in this area, feel free to contact the author via email [zhongyinmin@pku.edu.cn](mailto:zhongyinmin@pku.edu.cn).

docs/使用指南.md

@@ -10,11 +10,11 @@
 
 其一就是了解如何写“优雅”的代码。国内的很多大一编程入门课都会讲成极其无聊的语法课，其效果还不如直接让学生看官方文档。事实上，在刚开始接触编程的时候，让学生试着去了解什么样的代码是优雅的，什么样的代码 "have bad taste" 是大有裨益的。一般来说，编程入门课会先介绍过程式编程（例如 C 语言）。但即便是面向过程编程，**模块化** 和 **封装** 的思想也极其重要。如果你只想着代码能在 OpenJudge 上通过，写的时候图省事，用大段的复制粘贴和臃肿的 main 函数，长此以往，你的代码质量将一直如此。一旦接触稍微大一点的项目，无尽的 debug 和沟通维护成本将把你吞没。因此，写代码时不断问自己，是否有大量重复的代码？当前函数是否过于复杂（Linux 提倡每个函数只需要做好一件事）？这段代码能抽象成一个函数吗？一开始你可能觉得很不习惯，甚至觉得这么简单的题需要如此大费周章吗？但记住好的习惯是无价的，C 语言初中生都能学会，凭什么公司要招你去当程序员呢？
 
-学过面向过程编程后，大一下学期一般会讲面向对象编程（例如 C++ 或 Java）。这里非常推荐大家看 [MIT 6.031: Software Construction](./软件工程/6031.md) 这门课的 Notes，会以 Java 语言为例非常详细地讲解如何写出“优雅”的代码。例如 Test-Driven 的开发、函数 Specification 的设计、异常的处理等等等等。除此之外，既然接触了面向对象，那么了解一些常见的设计模式也是很有必要的。因为国内的面向对象课程同样很容易变成极其无聊的语法课，让学生纠结于各种继承的语法，甚至出一些无聊的脑筋急转弯一样的题目，殊不知这些东西在地球人的开发中基本不会用到。面向对象的精髓是让学生学会自己将实际的问题抽象成若干类和它们之间的关系，而设计模式则是前人总结出来的一些精髓的抽象方法。这里推荐[大话设计模式](https://book.douban.com/subject/2334288/) 这本书，写得非常浅显易懂。
+学过面向过程编程后，大一下学期一般会讲面向对象编程（例如 C++ 或 Java）。这里非常推荐大家看 [MIT 6.031: Software Construction](./软件工程/6031.md) 这门课的 Notes，会以 Java 语言（22年改用了 TypeScript 语言）为例非常详细地讲解如何写出“优雅”的代码。例如 Test-Driven 的开发、函数 Specification 的设计、异常的处理等等等等。除此之外，既然接触了面向对象，那么了解一些常见的设计模式也是很有必要的。因为国内的面向对象课程同样很容易变成极其无聊的语法课，让学生纠结于各种继承的语法，甚至出一些无聊的脑筋急转弯一样的题目，殊不知这些东西在地球人的开发中基本不会用到。面向对象的精髓是让学生学会自己将实际的问题抽象成若干类和它们之间的关系，而设计模式则是前人总结出来的一些精髓的抽象方法。这里推荐[大话设计模式](https://book.douban.com/subject/2334288/) 这本书，写得非常浅显易懂。
 
-其二就是尝试学习一些能提高生产力的工具和技能，例如 Git、Shell、Vim。这里强烈推荐学习 [MIT missing semester](./编程入门/MIT-Missing-Semester.md) 这门课，也许一开始接触这些工具用起来会很不习惯，但强迫自己用，熟练之后开发效率会直线提高。此外，还有很多应用也能极大提高的你生产力。一条定律是：一切需要让手离开键盘的操作，都应该想办法去除。例如切换应用、打开文件、浏览网页这些都有相关插件可以实现快捷操作（例如 Mac 上的 [Alfred](https://www.alfredapp.com/)）。如果你发现某个操作每天都会用到，并且用时超过1秒，那就应该想办法把它缩减到0.1秒。毕竟以后数十年你都要和电脑打交道，形成一套顺滑的工作流是事半功倍的。最后，学会盲打！如果你还需要看着键盘打字，那么赶紧上网找个教程学会盲打，这将极大提高你的开发效率。
+其二就是尝试学习一些能提高生产力的工具和技能，例如 Git、Shell、Vim。这里强烈推荐学习 [MIT missing semester](./编程入门/MIT-Missing-Semester.md) 这门课，也许一开始接触这些工具用起来会很不习惯，但强迫自己用，熟练之后开发效率会直线提高。此外，还有很多应用也能极大提高你的生产力。一条定律是：一切需要让手离开键盘的操作，都应该想办法去除。例如切换应用、打开文件、浏览网页这些都有相关插件可以实现快捷操作（例如 Mac 上的 [Alfred](https://www.alfredapp.com/)）。如果你发现某个操作每天都会用到，并且用时超过1秒，那就应该想办法把它缩减到0.1秒。毕竟以后数十年你都要和电脑打交道，形成一套顺滑的工作流是事半功倍的。最后，学会盲打！如果你还需要看着键盘打字，那么赶紧上网找个教程学会盲打，这将极大提高你的开发效率。
 
-其三就是平衡好课内和自学。我们质疑现状，但也得遵守规则，毕竟绩点在保研中还是相当重要的。因此在大一，我还是建议大家尽量按照自己的课表学习，但辅以一些优质的课外资源。例如微积分线代可以参考 [MIT 18.01/18.02](./数学基础/MITmaths.md) 和 [MIT 18.06](./数学基础/MITLA.md) 的课程 Notes。假期可以通过 [UCB CS61A](./编程入门/CS61A.md) 来学习 Python。同时做到上面第一、第二点说的，注重好的编程习惯和实践能力的培养。就个人经验，大一的数学课学分占比相当大，而且数学考试的内容方差是很大的，不同学校不同老师风格迥异，自学也许能让你领悟数学的本质，但未必能给你一个好成绩。因此考前最好有针对性地刷往年题，充分应试。
+其三就是平衡好课内和自学。我们质疑现状，但也得遵守规则，毕竟绩点在保研中还是相当重要的。因此在大一，我还是建议大家尽量按照自己的课表学习，但辅以一些优质的课外资源。例如微积分线代可以参考 [MIT 18.01/18.02](./数学基础/MITmaths.md) 和 [MIT 18.06](./数学基础/MITLA.md) 的课程 Notes。假期可以通过 [UCB CS61A](./编程入门/Python/CS61A.md) 来学习 Python。同时做到上面第一、第二点说的，注重好的编程习惯和实践能力的培养。就个人经验，大一的数学课学分占比相当大，而且数学考试的内容方差是很大的，不同学校不同老师风格迥异，自学也许能让你领悟数学的本质，但未必能给你一个好成绩。因此考前最好有针对性地刷往年题，充分应试。
 
 在升入大二之后，计算机方向的专业课将居多，此时大家可以彻底放飞自我，进入自学的殿堂了。具体可以参考 [一份仅供参考的CS学习规划](./CS学习规划.md)，这是我根据自己三年自学经历总结提炼出来的全套指南，每门课的特点以及为什么要上这门课我都做了简单的介绍。对于你课表上的每个课程，这份规划里应该都会有相应的国外课程，而且在质量上我相信基本是全方位的碾压。由于计算机方向的专业知识基本是一样的，而且高质量的课程会让你从原理上理解知识点，对于国内大多照本宣科式的教学来说基本是降维打击。一般来说只要考前将老师“辛苦”念了一学期的 PPT 拿来突击复习两天，取得一个不错的卷面分数并不困难。如果有课程大作业，则可以尽量将国外课程的 Lab 或者 Project 修改一番以应付课内的需要。我当时上操作系统课，发现老师还用着早已被国外学校淘汰的课程实验，便邮件老师换成了自己正在学习的 [MIT 6.S081](./操作系统/MIT6.S081.md) 的 xv6 Project，方便自学的同时还无意间推动了课程改革。总之，灵活变通是第一要义，你的目标是用最方便、效率最高的方式掌握知识，所有与你这一目标违背的所谓规定都可以想方设法地去“糊弄”。凭着这份糊弄劲儿，我大三之后基本没有去过线下课堂（大二疫情在家呆了大半年），对绩点也完全没有影响。
 
@@ -30,7 +30,7 @@
 |数据结构与算法 |[Coursera: Algorithms I & II](数据结构与算法/Algo.md)|
 |软件工程      |[MIT 6.031: Software Construction](软件工程/6031.md)|
 |全栈开发      |[MIT web development course](Web开发/mitweb.md)|
-|计算机系统导论 |[CMU CS15213: CSAPP](./体系结构/CSAPP.md)|
+|计算机系统导论 |[CMU CS15213: CSAPP](计算机系统基础/CSAPP.md)|
 |体系结构入门   |[Coursera: Nand2Tetris](./体系结构/N2T.md)       |
 |体系结构进阶   |[CS61C: Great Ideas in Computer Architecture](./体系结构/CS61C.md)|
 |数据库原理     |[CMU 15-445: Introduction to Database System](数据库系统/15445.md)|
@@ -42,6 +42,6 @@
 
 如果你对于计算机领域的核心专业课都掌握得相当扎实，而且已经确定了自己的工作或研究方向，那么书中还有很多未在 [一份仅供参考的CS学习规划](./CS学习规划.md) 提到的课程供你探索。
 
-随着贡献者的不断增多，左侧的目录中将不断增加新的分支，例如 **机器学习进阶** 和 **机器学习系统**。并且同一个分支下都有若干同类型课程，它们来自不同的学校，有着不同的侧重点和课程实验，例如 **操作系统** 分支下就包含了麻省理工、伯克利还有南京大学三个学校的课程。如果你想深耕一个领域，那么学习这些同类的课程会给你不同的角度来看待类似的知识。同时，本书作者还计划联系一些相关领域的科研工作者来分享某个细分领域的科研学习路径，让 CS自学指南 在追求广度的同时，实现深度上的提高。
+随着贡献者的不断增多，左侧的目录中将不断增加新的分支，例如 **机器学习进阶** 和 **机器学习系统**。并且同一个分支下都有若干同类型课程，它们来自不同的学校，有着不同的侧重点和课程实验，例如 **操作系统** 分支下就包含了麻省理工、伯克利、南京大学还有哈工大四所学校的课程。如果你想深耕一个领域，那么学习这些同类的课程会给你不同的视角来看待类似的知识。同时，本书作者还计划联系一些相关领域的科研工作者来分享某个细分领域的科研学习路径，让 CS自学指南 在追求广度的同时，实现深度上的提高。
 
-如果你想贡献这方面的内容，欢迎和作者邮件联系 [zhongyinmin@pku.edu.cn](mailto:zhongyinmin@pku.edu.cn)
+如果你想贡献这方面的内容，欢迎和作者邮件联系 [zhongyinmin@pku.edu.cn](mailto:zhongyinmin@pku.edu.cn)

docs/培养方案Pro.md

@@ -1 +0,0 @@
-under construction.

docs/好书推荐.md

@@ -10,7 +10,7 @@
         先中文后英文，同种语言先开源后闭源，最后按从基础到深入或者字母序。
  -->
 
-由于版权原因，下面列举的图书中除了开源资源提供了链接，其他的资源请大家自行通过 [libgen](http://libgen.is/) 或 [z-lib](https://z-lib.org/) 查找。
+由于版权原因，下面列举的图书中除了开源资源提供了链接，其他的资源请大家自行通过 [libgen](http://libgen.is/) 查找。
 
 ## 资源汇总
 
@@ -34,6 +34,7 @@
 - [Operating Systems: Three Easy Pieces](https://pages.cs.wisc.edu/~remzi/OSTEP/) [[豆瓣](https://book.douban.com/subject/19973015/)]
 - Modern Operating Systems [[豆瓣](https://book.douban.com/subject/27096665/)]
 - Operating Systems: Principles and Practice [[豆瓣](https://book.douban.com/subject/25984145/)]
+- [Operating Systems: Internals and Design Principles](https://elibrary.pearson.de/book/99.150005/9781292214306) [[豆瓣](https://book.douban.com/subject/6047741/)]
 
 ## 计算机网络
 
@@ -70,8 +71,7 @@
 ## 体系结构
 
 - 超标量处理器设计: Superscalar RISC Processor Design [[豆瓣](https://book.douban.com/subject/26293546/)]
-- Computer Organization and Design RISC-V Edition [[豆瓣](https://book.douban.com/subject/27103952/)]
-- Computer Organization and Design: The Hardware/Software Interface [[豆瓣](https://book.douban.com/subject/26604008/)]
+- Computer Organization and Design: The Hardware/Software Interface [[MIPS Edition](https://book.douban.com/subject/35998323/)][[ARM Edition](https://book.douban.com/subject/30443432/)][[RISC-V Edition](https://book.douban.com/subject/36490912/)]
 - Computer Architecture: A Quantitative Approach [[豆瓣](https://book.douban.com/subject/6795919/)]
 
 ## 理论计算机科学
@@ -118,6 +118,7 @@
 
 ## 深度学习
 
+- 深度学习 [[豆瓣](https://book.douban.com/subject/27087503/)][[Github](https://github.com/exacity/deeplearningbook-chinese)]
 - [动手学深度学习](https://zh.d2l.ai) [[豆瓣](https://book.douban.com/subject/33450010/)]
 - [神经网络与深度学习](https://nndl.github.io/) [[豆瓣](https://book.douban.com/subject/35044046/)]
 - 深度学习入门 [[豆瓣](https://book.douban.com/subject/30270959/)]

docs/并行与分布式系统/CS149.en.md

@@ -15,7 +15,7 @@ The goal of this course is to provide a deep understanding of the fundamental pr
 ## Resources
 
 - Course Website: [CMU15418](http://15418.courses.cs.cmu.edu/spring2016/), [CS149](https://gfxcourses.stanford.edu/cs149/fall21)
-- Recordings: <http://15418.courses.cs.cmu.edu/spring2016/lectures>
+- Recordings: [CMU15418](http://15418.courses.cs.cmu.edu/spring2016/lectures), [CS149](https://youtube.com/playlist?list=PLoROMvodv4rMp7MTFr4hQsDEcX7Bx6Odp&si=txtQiRDZ9ZZUzyRn)
 - Textbook: None
 - Assignments: <https://gfxcourses.stanford.edu/cs149/fall21>, 5 assignments.
 

docs/并行与分布式系统/CS149.md

@@ -14,8 +14,8 @@
 
 ## 课程资源
 
-- 课程网站：[CMU15418](http://15418.courses.cs.cmu.edu/spring2016/), [CS149](https://gfxcourses.stanford.edu/cs149/fall21)
-- 课程视频：<http://15418.courses.cs.cmu.edu/spring2016/lectures>
+- 课程网站：[CMU15418](https://www.cs.cmu.edu/afs/cs/academic/class/15418-s18/www/index.html), [CS149](https://gfxcourses.stanford.edu/cs149/fall21)
+- 课程视频：[CMU15418](https://www.cs.cmu.edu/afs/cs/academic/class/15418-s18/www/schedule.html), [CS149](https://youtube.com/playlist?list=PLoROMvodv4rMp7MTFr4hQsDEcX7Bx6Odp&si=txtQiRDZ9ZZUzyRn)
 - 课程教材：无
 - 课程作业：<https://gfxcourses.stanford.edu/cs149/fall21>，5 个编程作业
 

docs/必学工具/Docker.en.md

@@ -0,0 +1,15 @@
+# Docker
+
+## Why Docker
+
+The main obstacle when using software/tools developed by others is often the hassle of setting up the environment. This configuration headache can significantly dampen your enthusiasm for software and programming. While virtual machines can solve some of these issues, they are cumbersome and might not be worth simulating an entire operating system for a single application's configuration.
+
+[Docker](https://www.docker.com/) has changed the game by making environment configuration (potentially) less painful. In essence, Docker uses lightweight "containers" instead of an entire operating system to support an application's configuration. Applications, along with their environment configurations, are packaged into images that can freely run on different platforms in containers, saving considerable time and effort for everyone.
+
+## How to learn Docker
+
+The [official Docker documentation](https://docs.docker.com/) is the best starting point, but the best teacher is often yourself—try using Docker to experience its convenience. Docker has rapidly developed in the industry and is already quite mature. You can download its desktop version and use the graphical interface.
+
+If you're like me, reinventing the wheel, consider building a [Mini Docker](https://github.com/PKUFlyingPig/rubber-docker) yourself to deepen your understanding.
+
+[KodeKloud Docker for the Absolute Beginner](https://kodekloud.com/courses/docker-for-the-absolute-beginner/) offers a comprehensive introduction to Docker's basic functionalities with numerous hands-on exercises. It also provides a free cloud environment for practice. While other cloud-related courses, such as Kubernetes, may require payment, I highly recommend them. The explanations are detailed, suitable for beginners, and come with a corresponding Kubernetes lab environment, eliminating the need for complex setups.

docs/必学工具/GNU_Make.en.md

@@ -0,0 +1,13 @@
+# GNU Make
+
+## Why GNU Make
+
+Everyone remembers their first "hello world" program. After editing `helloworld.c`, you needed to use `gcc` to compile and generate an executable file, and then execute it. (If you're not familiar with this, please Google *gcc compilation* and understand the related content first.) However, what if your project consists of hundreds of C source files scattered across various subdirectories? How do you compile and link them together? Imagine if your project takes half an hour to compile (quite common for large projects), and you only changed a semicolon—would you want to wait another half an hour?
+
+This is where GNU Make comes to the rescue. It allows you to define the entire compilation process and the dependencies between target files and source files in a script (known as a `Makefile`). It only recompiles the parts affected by your changes, significantly reducing compilation time.
+
+## How to learn GNU Make
+
+Here is a well-written [document] (https://seisman.github.io/how-to-write-makefile/overview.html) for in-depth and accessible understanding.
+
+Mastering GNU Make is relatively easy, but using it effectively requires continuous practice. Integrate it into your daily development routine, be diligent in learning, and mimic the `Makefile` styles from other excellent open-source projects. Develop your own template that suits your needs, and over time, you will become more proficient in using GNU Make.

docs/必学工具/Git.en.md

@@ -13,7 +13,8 @@ Git is a powerful tool and when you finally master it, you will find all the eff
 Different from Vim, I don't suggest beginners use Git rashly without fully understanding it, because its inner logic can not be acquainted by practicing. Here is my recommended learning path: 
 
 1. Read this [Git tutorial](https://missing.csail.mit.edu/2020/version-control/) in English, or you can watch this [Git tutorial (by 尚硅谷)](https://www.bilibili.com/video/BV1vy4y1s7k6) in Chinese. 
-2. Read Chap1 - Chap5 of this open source book [Pro Git](https://git-scm.com/book/en/v2). Yes, to learn Git, you need to read a book. 
-3. Now that you have understood its principles and most of its usages, it's time to consolidate those commands by practicing. How to use Git properly is a kind of philosophy. I recommend reading this blog [How to Write a Git Commit Message](https://chris.beams.io/posts/git-commit/). 
-4. You are now in love with Git and are not content with only using it, you want to build a Git by yourself! Great, that's exactly what I was thinking. [This tutorial](https://wyag.thb.lt/) will satisfy you! 
-5. What? Building your own Git is not enough?  Seems that you are also passionate about reinventing the wheels. These two GitHub projects, [build-your-own-x](https://github.com/danistefanovic/build-your-own-x) and [project-based-learning](https://github.com/tuvtran/project-based-learning), collected many wheel-reinventing tutorials, e.g., text editor, virtual machine, docker, TCP and so on. 
+2. Read Chap1 - Chap5 of this open source book [Pro Git](https://git-scm.com/book/en/v2). Yes, to learn Git, you need to read a book.
+3. [Learn Git Branching](https://learngitbranching.js.org/) is an interactive Git learning website that can help you quickly get started with using Git.
+4. Now that you have understood its principles and most of its usages, it's time to consolidate those commands by practicing. How to use Git properly is a kind of philosophy. I recommend reading this blog [How to Write a Git Commit Message](https://chris.beams.io/posts/git-commit/). 
+5. You are now in love with Git and are not content with only using it, you want to build a Git by yourself! Great, that's exactly what I was thinking. [This tutorial](https://wyag.thb.lt/) will satisfy you! 
+6. What? Building your own Git is not enough?  Seems that you are also passionate about reinventing the wheels. These two GitHub projects, [build-your-own-x](https://github.com/danistefanovic/build-your-own-x) and [project-based-learning](https://github.com/tuvtran/project-based-learning), collected many wheel-reinventing tutorials, e.g., text editor, virtual machine, docker, TCP and so on. 

docs/必学工具/Git.md

@@ -14,6 +14,7 @@ Git 的设计非常优雅，但初学者通常因为很难理解其内部逻辑
 
 1. 阅读这篇 [Git tutorial](https://missing.csail.mit.edu/2020/version-control/)，视频的话可以看这个[尚硅谷Git教程](https://www.bilibili.com/video/BV1vy4y1s7k6)
 2. 阅读这本开源书籍 [Pro Git](https://git-scm.com/book/en/v2) 的 Chapter1 - Chapter5，是的没错，学 Git 需要读一本书（捂脸）。
-3. 此时你已经掌握了 Git 的原理和绝大部分用法，接下来就可以在实践中反复巩固 Git 的命令了。但用好它同样是一门哲学，我个人觉得这篇[如何写好 Commit Message](https://chris.beams.io/posts/git-commit/) 的博客非常值得一读。
-4. 好的此时你已经爱上了 Git，你已经不满足于学会它了，你想自己实现一个 Git！巧了，我当年也有这样的想法，[这篇 tutorial](https://wyag.thb.lt/) 可以满足你！
-5. 什么？光实现一个 Git 无法满足你？小伙子/小仙女有前途，巧的是我也喜欢造轮子，这两个 GitHub 项目 [build-your-own-x](https://github.com/danistefanovic/build-your-own-x) 和 [project-based-learning](https://github.com/tuvtran/project-based-learning) 收录了你能想到的各种造轮子教程，比如：自己造个编辑器、自己写个虚拟机、自己写个 docker、自己写个 TCP 等等等等。
+3. [Learn Git Branching](https://learngitbranching.js.org/) 是一个交互式的 Git 学习网站, 可以帮助你快速上手 Git 的使用。
+4. 此时你已经掌握了 Git 的原理和绝大部分用法，接下来就可以在实践中反复巩固 Git 的命令了。但用好它同样是一门哲学，我个人觉得这篇[如何写好 Commit Message](https://chris.beams.io/posts/git-commit/) 的博客非常值得一读。
+5. 好的此时你已经爱上了 Git，你已经不满足于学会它了，你想自己实现一个 Git！巧了，我当年也有这样的想法，[这篇 tutorial](https://wyag.thb.lt/) 可以满足你！
+6. 什么？光实现一个 Git 无法满足你？小伙子/小仙女有前途，巧的是我也喜欢造轮子，这两个 GitHub 项目 [build-your-own-x](https://github.com/danistefanovic/build-your-own-x) 和 [project-based-learning](https://github.com/tuvtran/project-based-learning) 收录了你能想到的各种造轮子教程，比如：自己造个编辑器、自己写个虚拟机、自己写个 docker、自己写个 TCP 等等等等。

docs/必学工具/GitHub.en.md

@@ -0,0 +1,17 @@
+# GitHub
+
+## What is GitHub
+
+Functionally, GitHub is an online platform for hosting code. You can host your local Git repositories on GitHub for collaborative development and maintained by a group. However, GitHub's significance has evolved far beyond that. It has become a very active and resource-rich open-source community. Developers from all over the world share a wide variety of open-source software on GitHub. From industrial-grade deep learning frameworks like PyTorch and TensorFlow to practical scripts consisting of just a few lines of code, GitHub offers hardcore knowledge sharing, beginner-friendly tutorials, and even many technical books are open-sourced here (like the one you're reading now). Browsing GitHub has become a part of my daily life.
+
+On GitHub, stars are the ultimate affirmation for a project. If you find this book useful, you are welcome to enter the repository's homepage via the link in the upper right corner and give your precious star✨.
+
+## How to Use GitHub
+
+If you have never created your own remote repository on GitHub or cloned someone else's code, I suggest you start your open-source journey with [GitHub's official tutorial](https://docs.github.com/en/get-started).
+
+If you want to keep up with some interesting open-source projects on GitHub, I highly recommend the [HelloGitHub](https://hellogithub.com/) website. It regularly features GitHub's recently trending or very interesting open-source projects, giving you the opportunity to access various quality resources firsthand.
+
+I believe GitHub's success is due to the "one for all, all for one" spirit of open source and the joy of sharing knowledge. If you also want to become the next revered open-source giant or the author of a project with tens of thousands of stars, then transform your ideas that spark during development into code and showcase them on GitHub.
+
+However, it's important to note that the open-source community is not lawless. Many open-source softwares are not meant for arbitrary copying, distribution, or even sale. Understanding various [open-source licenses](https://www.runoob.com/w3cnote/open-source-license.html) and complying with them is not only a legal requirement but also the responsibility of every member of the open-source community.

docs/必学工具/LaTeX.en.md

@@ -0,0 +1,32 @@
+# LaTeX
+
+## Why Learn LaTeX
+
+If you need to write academic papers, please skip directly to the next section, as learning LaTeX is not just a choice but a necessity.
+
+LaTeX is a typesetting system based on TeX, developed by Turing Award winner Lamport, while TeX was originally developed by Knuth, both of whom are giants in the field of computer science. Of course, the developers' prowess is not the reason we learn LaTeX. The biggest difference between LaTeX and the commonly used WYSIWYG (What You See Is What You Get) Word documents is that in LaTeX, users only need to focus on the content of the writing, leaving the typesetting entirely to the software. This allows people without any typesetting experience to produce papers or articles with highly professional formatting.
+
+Berkeley computer science professor Christos Papadimitriou once jokingly said:
+
+> Every time I read a LaTeX document, I think, wow, this must be correct!
+
+## How to Learn LaTeX
+
+The recommended learning path is as follows:
+
+- Setting up the LaTeX environment can be a headache. If you encounter problems with configuring LaTeX locally, consider using [Overleaf], an online LaTeX editor. The site not only offers a variety of LaTeX templates to choose from but also eliminates the difficulty of environment setup.
+- Read the following three tutorials: [Part-1], [Part-2], [Part-3].
+- The best way to learn LaTeX is, of course, by writing papers. However, starting with a math class and using LaTeX for homework is also a good choice.
+
+[Overleaf]: https://www.overleaf.com
+[Part-1]: https://www.overleaf.com/latex/learn/free-online-introduction-to-latex-part-1
+[Part-2]: https://www.overleaf.com/latex/learn/free-online-introduction-to-latex-part-2
+[Part-3]: https://www.overleaf.com/latex/learn/free-online-introduction-to-latex-part-3
+
+Other recommended introductory materials include:
+
+- A brief guide to installing LaTeX [[GitHub](https://github.com/OsbertWang/install-latex-guide-zh-cn)] or the TEX Live Guide (texlive-zh-cn) [[PDF](https://www.tug.org/texlive/doc/texlive-zh-cn/texlive-zh-cn.pdf)] can help you with installation and environment setup.
+- A (not so) brief introduction to LaTeX2ε (lshort-zh-cn) [[PDF](https://mirrors.ctan.org/info/lshort/chinese/lshort-zh-cn.pdf)] [[GitHub](https://github.com/CTeX-org/lshort-zh-cn)], translated by the CTEX development team, helps you get started quickly and accurately. It's recommended to read it thoroughly.
+- Liu Haiyang's "Introduction to LaTeX" can be used as a reference book, to be consulted when you have specific questions. Skip the section on CTEX suite.
+- [Modern LaTeX Introduction Seminar](https://github.com/stone-zeng/latex-talk)
+- [A Very Short LaTeX Introduction Document](https://liam.page/2014/09/08/latex-introduction/)

docs/必学工具/Scoop.en.md

@@ -0,0 +1,50 @@
+# Scoop
+
+## Why Use Scoop
+
+Setting up a development environment in Windows has always been a complex and challenging task. The lack of a unified standard means that the installation methods for different development environments vary greatly, resulting in unnecessary time costs. Scoop helps you uniformly install and manage common development software, eliminating the need for manual downloads, installations, and environment variable configurations.
+
+For example, to install Python and Node.js, you just need to execute:
+
+```powershell
+scoop install python
+scoop install nodejs
+```
+
+## Installing Scoop
+
+Scoop requires [Windows PowerShell 5.1](https://aka.ms/wmf5download) or [PowerShell](https://aka.ms/powershell) as its runtime environment. If you are using Windows 10 or later, Windows PowerShell is built into the system. However, the version of Windows PowerShell built into Windows 7 is outdated, and you will need to manually install a newer version of PowerShell.
+
+> Many students have encountered issues due to setting up Windows user accounts with Chinese usernames, leading to user directories also being named in Chinese. Installing software via Scoop into user directories in such cases may cause some software to execute incorrectly. Therefore, it is recommended to install in a custom directory. For other installation methods, please refer to: [ScoopInstaller/Install](https://github.com/ScoopInstaller/Install)
+
+```powershell
+# Set PowerShell execution policy
+Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser
+# Download the installation script
+irm get.scoop.sh -outfile 'install.ps1'
+# Run the installation, use --ScoopDir parameter to specify Scoop installation path
+.\install.ps1 -ScoopDir 'C:\Scoop'
+```
+
+## Using Scoop
+
+Scoop's official documentation is very user-friendly for beginners. Instead of elaborating here, it is recommended to read the [official documentation](https://github.com/ScoopInstaller/Scoop) or the [Quick Start guide](https://github.com/ScoopInstaller/Scoop/wiki/Quick-Start).
+
+## Q&A
+
+### Can Scoop Configure Mirror Sources?
+
+The Scoop community only maintains installation configurations, and all software is downloaded from the official download links provided by the software's creators. Therefore, mirror sources are not provided. If your network environment causes repeated download failures, you may need a bit of [magic](翻墙.md).
+
+### Why Can't I Find Java 8?
+
+For the same reasons mentioned above, the official download links for Java 8 are no longer provided. It is recommended to use [ojdkbuild8](https://github.com/ScoopInstaller/Java/blob/master/bucket/ojdkbuild8.json) as a substitute.
+
+### How Do I Install Python 2?
+
+For software that is outdated and no longer in use, the Scoop community removes it from [ScoopInstaller/Main](https://github.com/ScoopInstaller/Main) and adds it to [ScoopInstaller/Versions](https://github.com/ScoopInstaller/Versions). If you need such software, you need to manually add the bucket:
+
+```powershell
+scoop bucket add versions
+scoop install python27
+```

docs/必学工具/Scoop.md

@@ -34,7 +34,7 @@ Scoop 的官方文档对于新手非常友好，相对于在此处赘述更推
 
 ### Scoop 能配置镜像源吗？
 
-Scoop 社区仅维护安装配置，所有的软件都是从该软件官方提供的下载链接进行下载，所以无法提供镜像源。如果因为你的网络环境导致多次下载失败，那么你需要一点点 [魔法](/必学工具/翻墙/)。
+Scoop 社区仅维护安装配置，所有的软件都是从该软件官方提供的下载链接进行下载，所以无法提供镜像源。如果因为你的网络环境导致多次下载失败，那么你需要一点点 [魔法](翻墙.md)。
 
 ### 为什么找不到 Java8？
 

docs/必学工具/Vim.md

@@ -27,7 +27,8 @@ Vim 的学习资料浩如烟海，但掌握它最好的方式还是将它用在
 用 Vim 编辑代码的时候会频繁用到 ESC 和 CTRL 键, 但是这两个键都离 home row 很远, 可以把 CapsLock 键映射到 Esc 或者 Ctrl 键，让手更舒服一些。
 
 Windows 系统可以使用 [Powertoys](https://learn.microsoft.com/en-us/windows/powertoys/) 或者 [AutoHotkey](https://www.autohotkey.com/) 重映射键位。    
-MacOS 系统提供了重映射键位的[设置](https://vim.fandom.com/wiki/Map_caps_lock_to_escape_in_macOS)，另外也可以使用 [Karabiner-Elements](https://karabiner-elements.pqrs.org/) 重映射。
+MacOS 系统提供了重映射键位的[设置](https://vim.fandom.com/wiki/Map_caps_lock_to_escape_in_macOS)，另外也可以使用 [Karabiner-Elements](https://karabiner-elements.pqrs.org/) 重映射。  
+Linux 系统可以使用 [xremap](https://github.com/xremap/xremap) 进行映射，对于 wayland 和 x.org 都可以使用，并且支持分别映射点按和按住。
 
 但更佳的做法是同时将 CapsLock 映射为 Ctrl 和 Esc，点按为 Esc，按住为 Ctrl。这是不同系统下的实现方法：
 

docs/必学工具/thesis.en.md

@@ -0,0 +1,15 @@
+# Thesis Writing
+
+## Why I Wrote This Tutorial
+
+In 2022, I graduated from my college. When I started writing my thesis, I embarrassingly realized that my command of Word was limited to basic functions like adjusting fonts and saving documents. I considered switching to LaTeX, but formatting requirements for the thesis were more conveniently handled in Word. After a painful struggle, I finally completed the writing and defense of my thesis. To prevent others from following in my footsteps, I compiled relevant resources into a ready-to-use document for everyone's reference.
+
+## How to Write a Graduation Thesis in Word
+
+Just as it takes three steps to put an elephant in a fridge, writing a graduation thesis in Word also requires three simple steps:
+
+1. **Determine the Format Requirements of the Thesis**: Usually, colleges will provide the formatting requirements for theses (font and size for headings, sections, formatting of figures and citations, etc.), and if you're lucky, they might even provide a thesis template (if so, jump to the next step). Unfortunately, my college did not issue standard format requirements and provided a chaotic and almost useless template. Out of desperation, I found the [thesis format requirements](https://github.com/PKUFlyingPig/Thesis-Template/blob/master/%E5%8C%97%E4%BA%AC%E5%A4%A7%E5%AD%A6%E7%A0%94%E7%A9%B6%E7%94%9F%E5%AD%A6%E4%BD%8D%E8%AE%BA%E6%96%87%E5%86%99%E4%BD%9C%E6%8C%87%E5%8D%97.pdf) of Peking University graduate students and created [a template](https://github.com/PKUFlyingPig/Thesis-Template/blob/master/%E8%AE%BA%E6%96%87%E6%A8%A1%E7%89%88.docx) based on their guidelines. Feel free to use it, but I take no responsibility for any issues for using it.
+
+2. **Learn Word Formatting**: At this stage, you either have a standard template provided by your college or just a vague set of formatting requirements. Now, the priority is to learn basic Word formatting skills. If you have a template, learn to use it; if not, learn to create one. Remember, there's no need to ambitiously start with a lengthy Word tutorial video. A half-hour tutorial is enough to get started for creating a passable academic paper. I watched [a concise and practical Bilibili tutorial video](https://www.bilibili.com/video/BV1YQ4y1M73G?p=1&vd_source=a4d76d1247665a7e7bec15d15fd12349), which is very useful for a quick start.
+
+3. **Produce Academic Work**: The easiest step. Everyone has their own way, so unleash your creativity. Best wishes for a smooth graduation!

docs/必学工具/tools.en.md

@@ -0,0 +1,103 @@
+# Practical Toolbox
+
+## Download Tools
+
+- [Sci-Hub](https://sci-hub.se/): A revolutionary site aiming to break knowledge barriers, greeted by the goddess Elbakyan.
+- [Library Genesis](http://libgen.is/): A website for downloading e-books.
+- [Z-library](https://z-library.rs/): An e-book download site (works better under [Tor](https://www.torproject.org/), [link](http://loginzlib2vrak5zzpcocc3ouizykn6k5qecgj2tzlnab5wcbqhembyd.onion/)).
+- [Z-ePub](https://z-epub.com/): ePub e-book download site.
+- [PDF Drive](https://www.pdfdrive.com/): A PDF e-book search engine.
+- [MagazineLib](https://magazinelib.com/): A site for downloading PDF e-magazines.
+- [BitDownloader](https://bitdownloader.io/): YouTube video downloader.
+- [qBittorrent](https://www.qbittorrent.org/download.php): A BitTorrent client.
+- [uTorrent](https://www.utorrent.com): Another BitTorrent client.
+- [National Standard Information Public Service Platform](https://std.samr.gov.cn/): Official platform for querying and downloading various standards.
+- [Standard Knowledge Service System](http://www.standards.com.cn/): Search and read the standards you need.
+- [MSDN, I Tell You](https://msdn.itellyou.cn/): A site for downloading Windows OS images and other software.
+
+## Design Tools
+
+- [excalidraw](https://excalidraw.com/): A hand-drawn style drawing tool, great for creating diagrams in course reports or PPTs.
+- [tldraw](https://www.tldraw.com/): A drawing tool suitable for flowcharts, architecture diagrams, etc.
+- [draw.io](https://app.diagrams.net/): A powerful and concise online drawing website, supports flowcharts, UML diagrams, architecture diagrams, prototypes, etc., with export options for Onedrive, Google Drive, Github, and offline client availability.
+- [origamiway](https://www.origamiway.com/paper-folding-crafts-step-by-step.shtml): Step-by-step origami tutorials.
+- [thingiverse](https://www.thingiverse.com/): Includes various 2D/3D design resources, with STL files ready for 3D printing.
+- [iconfont](https://www.iconfont.cn/): The largest icon and illustration library in China, useful for development or drawing system architecture diagrams.
+- [turbosquid](https://www.turbosquid.com/): A platform to purchase various models.
+- [flaticon](https://www.flaticon.com/): A site to download free and high-quality icons.
+- [Standard Map Service System](http://bzdt.ch.mnr.gov.cn/): Official standard map downloads.
+- [PlantUML](https://plantuml.com/zh/): Quickly write UML diagrams using code.
+
+## Programming Related
+
+- [sqlfiddle](http://www.sqlfiddle.com/): An easy-to-use online SQL Playground.
+- [sqlzoo](https://sqlzoo.net/wiki/SQL_Tutorial): Practice SQL statements online.
+- [godbolt](https://godbolt.org/): A convenient compiler exploration tool. Write some C/C++ code, choose a compiler, and observe the specific assembly code generated.
+- [explainshell](https://explainshell.com/): Struggling with the meaning of a shell command? Try this site!
+- [regex101](https://regex101.com/): A regex debugging site supporting various programming language standards.
+- [typingtom](https://www.typingtom.com/lessons): Typing practice/speed test site for programmers.
+- [wrk](https://github.com/wg/wrk): Website stress testing tool.
+- [gbmb](https://www.gbmb.org/): Data unit conversion tool.
+- [tools](https://tools.fun/): A collection of online tools.
+- [github1s](https://github1s.com/): Read GitHub code online with a web-based VS Code.
+- [visualgo](https://visualgo.net/en): Algorithm visualization website.
+- [DataStructureVisual](http://www.rmboot.com/): Data structure visualization website.
+- [Data Structure Visualizations](https://www.cs.usfca.edu/~galles/visualization/Algorithms.html): Visualization website for data structures and algorithms.
+- [learngitbranching](https://learngitbranching.js.org/?locale=zh_CN): Visualize learning git.
+- [UnicodeCharacter](https://unicode-table.com/en/): Unicode character set website.
+- [cyrilex](https://extendsclass.com/regex-tester.html): A site for testing and visualizing regular expressions, supporting various programming language standards.
+- [mockium](https://softwium.com/mockium/): Platform for generating test data.
+
+## Learning Websites
+
+- [HFS](https://hepsoftwarefoundation.org/training/curriculum.html): Various software tutorials.
+- [Shadertoy](https://www.shadertoy.com/): Write various shaders.
+- [comments-for-awesome-courses](https://conanhujinming.github.io/comments-for-awesome-courses/): Reviews of open courses from prestigious universities.
+- [codetop](https://codetop.cc/home): Corporate problem bank.
+- [cs-video-courses](https://github.com/Developer-Y/cs-video-courses): List of computer science courses with video lectures.
+- [bootlin](https://elixir.bootlin.com/linux/v2.6.39.4/source/include/linux): Read Linux source code online.
+- [ecust-CourseShare](https://github.com/tianyilt/ecnu-PGCourseShare): East China Normal University graduate course strategy sharing project.
+- [REKCARC-TSC-UHT](https://github.com/PKUanonym/REKCARC-TSC-UHT): Tsinghua University computer science course strategy.
+- [seu-master](https://github.com/oneman233/seu-master): Southeast University graduate course materials.
+- [Runoob](https://www.runoob.com/): Brief tutorials on computer-related knowledge.
+- [FreeBSD From Entry to Run Away](https://book.bsdcn.org/): A Chinese tutorial on FreeBSD.
+- [MDN Web Docs](https://developer.mozilla.org/zh-CN/docs/Learn): MDN's beginner's guide to web development.
+- [Hello Algorithm](https://www.hello-algo.com/): A quick introductory tutorial on data structures and algorithms with animations, runnable examples, and Q&A.
+
+## Encyclopedic/Dictionarial Websites
+
+- [os-wiki](https://wiki.osdev.org/Main_Page): An encyclopedia of operating system technology resources.
+- [FreeBSD Documentation](https://docs.freebsd.org/en/): Official FreeBSD documentation.
+- [Python3 Documentation](https://docs.python.org/zh-cn/3/): Official Chinese documentation for Python3.
+- [C++ Reference](https://en.cppreference.com/w/): C++ reference manual.
+- [OI Wiki](https://oi-wiki.org/): An integrated site for programming competition knowledge.
+- [Microsoft Learn](https://learn.microsoft.com/zh-cn/): Microsoft's official learning platform, containing most Microsoft product documentation.
+- [Arch Wiki](https://wiki.archlinux.org/): Wiki written for Arch Linux, containing a lot of Linux-related knowledge.
+- [Qt Wiki](https://wiki.qt.io/Main): Official Qt Wiki.
+- [OpenCV Chinese Documentation](https://opencv.apachecn.org/#/): Community version of OpenCV's Chinese documentation.
+- [npm Docs](https://docs.npmjs.com/): Official npm documentation.
+
+## Communication Platforms
+
+- [GitHub](https://github.com/): Many open-source projects' hosting platform, also a major communication platform for many open-source projects, where issues can solve many problems.
+- [StackExchange](https://stackexchange.com/): A programming community composed of 181 Q&A communities (including Stack Overflow).
+- [StackOverflow](https://stackoverflow.com/): An IT technical Q&A site related to programming.
+- [Gitee](https://gitee.com/): A code hosting platform similar to GitHub, where you can find solutions to common questions in the issues of corresponding projects.
+- [Zhihu](https://www.zhihu.com/): A Q&A community similar to Quora, where you can ask questions, with some answers containing computer knowledge.
+- [Cnblogs](https://www.cnblogs.com/): A knowledge-sharing community for developers, containing blogs on common questions. Accuracy is not guaranteed, please use with caution.
+- [CSDN](https://blog.csdn.net/): Contains blogs on common questions. Accuracy is not guaranteed, please use with caution.
+
+## Miscellaneous
+
+- [tophub](https://tophub.today/): A collection of trending news headlines (aggregating from Zhihu, Weibo, Baidu, WeChat, etc.).
+- [feedly](https://feedly.com/): A famous RSS feed reader.
+- [speedtest](https://www.speedtest.net/zh-Hans): An online network speed testing website.
+- [public-apis](https://github.com/public-apis/public-apis): A collective list of free APIs for development.
+- [numberempire](https://zh.numberempire.com/derivativecalculator.php): A tool for calculating derivatives of functions.
+- [sustech-application](https://sustech-application.com/#/grad-application/computer-science-and-engineering/README): Southern University of Science and Technology experience sharing website.
+- [vim-adventures](https://vim-adventures.com/): An online game based on vim keyboard shortcuts.
+- [vimsnake](https://vimsnake.com/): Play the snake game using vim commands.
+- [keybr](https://www.keybr.com/): A website for learning touch typing.
+- [Awesome C++](https://cpp.libhunt.com/): A curated list of awesome C/C++ frameworks, libraries, resources.
+- [HelloGitHub](https://hellogithub.com/): Shares interesting and beginner-friendly open-source projects on GitHub.
+- [Synergy](https://github.com/DEAKSoftware/Synergy-Binaries): A set of keyboard and mouse controls for multiple computers

docs/必学工具/tools.md

@@ -4,7 +4,7 @@
 
 - [Sci-Hub](https://sci-hub.se/): Elbakyan 女神向你挥手，旨在打破知识壁垒的革命性网站。
 - [Library Genesis](http://libgen.is/): 电子书下载网站。
-- [Z-library](https://z-lib.is/): 电子书下载网站（在 [Tor](https://www.torproject.org/) 下运行较佳，[链接](http://loginzlib2vrak5zzpcocc3ouizykn6k5qecgj2tzlnab5wcbqhembyd.onion/)）。
+- [Z-library](https://z-library.rs/): 电子书下载网站（在 [Tor](https://www.torproject.org/) 下运行较佳，[链接](http://loginzlib2vrak5zzpcocc3ouizykn6k5qecgj2tzlnab5wcbqhembyd.onion/)）。
 - [Z-ePub](https://z-epub.com/): ePub 电子书下载网站。
 - [PDF Drive](https://www.pdfdrive.com/): PDF 电子书搜索引擎。
 - [MagazineLib](https://magazinelib.com/): PDF 电子杂志下载网站。
@@ -32,6 +32,7 @@
 
 - [sqlfiddle](http://www.sqlfiddle.com/): 一个简易的在线 SQL Playground。
 - [sqlzoo](https://sqlzoo.net/wiki/SQL_Tutorial)：在线练习 sql 语句。
+- [sqlable](https://sqlable.com)：一个 SQL 工具网站（格式化器、验证器、生成器，SQL Playground）。
 - [godbolt](https://godbolt.org/): 非常方便的编译器探索工具。你可以写一段 C/C++ 代码，选择一款编译器，然后便可以观察生成的具体汇编代码。
 - [explainshell](https://explainshell.com/): 你是否曾为一段 shell 代码的具体含义感到困扰？manpage 看半天还是不明所以？试试这个网站！
 - [regex101](https://regex101.com/): 正则表达式调试网站，支持各种编程语言的匹配标准。
@@ -45,6 +46,8 @@
 - [Data Structure Visualizations](https://www.cs.usfca.edu/~galles/visualization/Algorithms.html): 数据结构与算法的可视化网站。
 - [learngitbranching](https://learngitbranching.js.org/?locale=zh_CN): 可视化学习 git。
 - [UnicodeCharacter](https://unicode-table.com/en/): Unicode 字符集网站。
+- [cyrilex](https://extendsclass.com/regex-tester.html): 一个用于测试和可视化正则表达式的网站，支持各种编程语言标准。
+- [mockium](https://softwium.com/mockium/): 生成测试数据的平台。
 
 ## 学习网站
 
@@ -96,3 +99,4 @@
 - [keybr](https://www.keybr.com/): 学习盲打的网站。
 - [Awesome C++](https://cpp.libhunt.com/): 很棒的 C/C++ 框架、库、资源精选列表。
 - [HelloGitHub](https://hellogithub.com/): 分享 GitHub 上有趣、入门级的开源项目。
+- [Synergy](https://github.com/DEAKSoftware/Synergy-Binaries): 一套键鼠能控制多台电脑。

docs/必学工具/workflow.en.md

@@ -0,0 +1,121 @@
+> Contributed by [@HardwayLinka](https://github.com/HardwayLinka)
+
+The field of computer science is vast and rapidly evolving, making lifelong learning crucial. However, our sources of knowledge in daily development and learning are complex and fragmented. We encounter extensive documentation manuals, brief blogs, and even snippets of news and public accounts on our phones that may contain interesting knowledge. Therefore, it's vital to use various tools to create a learning workflow that suits you, integrating these knowledge fragments into your personal knowledge base for easy reference and review. After two years of learning alongside work, I have developed the following learning workflow:
+
+![](https://raw.githubusercontent.com/HardwayLinka/image/master/Drawing 2022-10-20 11.23.41.excalidraw.png)
+
+## Core Logic
+
+Initially, when learning new knowledge, I referred to Chinese blogs but often found bugs and gaps in my code practice. Gradually, I realized that the information I referred to might be incorrect, as the threshold for posting blogs is low and their credibility is not high. So, I started consulting some related Chinese books.
+
+Chinese books indeed provide a comprehensive and systematic explanation of concepts. However, given the rapid evolution of computer technology and the US's leadership in CS, content in Chinese books often lags behind the latest knowledge. This led me to realize the importance of firsthand information. Some Chinese books are translations of English ones, and translation can take a year or two, causing a delay in information transmission and loss during translation. If a Chinese book is not a translation, it likely references other books, introducing biases in interpreting the original English text.
+
+Therefore, I naturally started reading English books. The quality of English books is generally higher than that of Chinese ones. As I delved deeper into my studies, I discovered a hierarchy of information reliability: `source code` > `official documentation` > `English books` > `English blogs` > `Chinese blogs`. This led me to create an "Information Loss Chart":
+
+![](https://cdn.sspai.com/2022/10/11/bf07c1965a2e5bdf3f00644737789e2e.png)
+
+Although firsthand information is crucial, subsequent iterations (N-th hand information) are not useless. They include the author's transformation of the source knowledge — such as logical organization (flow charts, mind maps) or personal interpretations (abstractions, analogies, extensions to other knowledge points). These transformations can help us quickly grasp and consolidate core knowledge, like using guidebooks in school. Moreover, interacting with others' interpretations during learning is important, allowing us to benefit from various perspectives. Hence, it's advisable to first choose high-quality, less distorted sources of information while also considering multiple sources for a more comprehensive and accurate understanding.
+
+In real-life work and study, learning rarely follows a linear, deep dive into a single topic. Often, it involves other knowledge points, such as new jargon, classic papers not yet read, or unfamiliar code snippets. This requires us to think deeply and "recursively" learn, establishing connections between multiple knowledge points.
+
+## Choosing the Right Note-taking Software
+
+The backbone of the workflow is built around the core logic of "multiple references for a single knowledge point and building connections among various points." This is similar to writing academic papers. Papers usually have footnotes explaining keywords and multiple references at the end. But our daily notes are much more casual, hence the need for a more flexible method.
+
+I'm accustomed to jumping to related functions and implementations in an IDE. It would be great if notes could also be interlinked like code. Current "double-link note-taking software," such as Roam Research, Logseq, Notion, and Obsidian, addresses this need. I chose Obsidian for the following reasons:
+
+- Obsidian is based locally, with fast opening speeds, and can store many e-books. My laptop, an Asus TUF Gaming FX505 with 32GB of RAM, runs Obsidian very smoothly.
+- Obsidian is Markdown-based. This is an advantage because if a note-taking software uses a proprietary format, it's inconvenient for third-party extensions and opening notes with other software.
+- Obsidian has a rich and active plugin ecosystem, allowing for an "all in one" effect, meaning various knowledge sources can be integrated in one place.
+
+## Information Sources
+
+Obsidian's plugins support PDF formats, and it naturally supports Markdown. To achieve "all in one," you can convert other file formats to PDF or Markdown. This presents two questions:
+
+- What formats are there?
+- How to convert them to PDF or Markdown?
+
+![](https://cdn.sspai.com/2022/10/11/3801b1c9b94286566fe677e3b12cc7b0.png)
+
+### Formats
+
+File formats depend on their display platforms. Before considering formats, let's list the sources of information I usually access:
+
+![](https://cdn.sspai.com/2022/10
+
+/11/07e97f372850054958d4961a3787a93f.png)
+
+The main categories are `articles`, `papers`, `e-books`, and `courses`, primarily including formats like `web pages`, `PDFs`, `MOBI`, `AZW`, and `AZW3`.
+
+### Conversion to PDF or Markdown
+
+Online articles and courses are mostly presented as web pages. To convert web pages to Markdown, I use the clipping software "Simplified Read," which can clip articles from nearly all platforms into Markdown and import them into Obsidian.
+
+![](https://cdn.sspai.com/2022/10/11/211cffa78f20a9e7286a7419e9e0b878.png)
+
+For papers and e-books, if the format is already PDF, it's straightforward. Otherwise, I use Calibre for conversion:
+
+![](https://cdn.sspai.com/2022/10/11/51575f65f6f4c6edfa6c5b97fd16d625.png)
+
+Now, using Obsidian's PDF plugin and native Markdown support, I can seamlessly take notes and reference across these documents (see "Information Processing" below for details).
+
+![](https://cdn.sspai.com/2022/10/11/d64a9a2d6406d2d367dcb505ede69c83.png)
+
+### Managing Information Sources
+
+For file resources like PDFs, I use local or cloud storage. For web resources, I categorize and save them in browser bookmarks or clip them into Markdown notes. However, browsers don't support mobile web bookmarking. To enable cross-platform web bookmarking, I use Cubox. With a swipe on my phone, I can save interesting web pages in one place. Although the free version limits to 100 bookmarks, it's usually sufficient and prompts me to process these pages promptly.
+
+![](https://cdn.sspai.com/2022/10/11/ad7ebfcb4619f64a41d328b88e0e3a12.png)
+
+Moreover, many of the web pages we bookmark are not from fully-featured blog platforms like Zhihu or Juejin but personal sites without mobile apps. These can be easily overlooked in browser bookmarks, and we might miss new article notifications. Here, `RSS` comes into play.
+
+`RSS` (Rich Site Summary) is a type of web feed that allows users to access updates to online content in a standardized format. On desktops, `RSSHub Radar` helps discover and generate `RSS` feeds, which can be subscribed to using `Feedly` (both have official Chrome browser plugins).
+
+![](https://cdn.sspai.com/2022/10/11/5df6cd9d967f190df35928e781f9185f.png)
+
+With this, the information collection process is comprehensive. But no matter how well categorized, information needs to be internalized to be useful. After collecting information, the next step is processing it — reading, understanding the semantics (especially for English sources), highlighting key sentences or paragraphs, noting queries, brainstorming related knowledge points, and writing summaries. What tools are needed for this process?
+
+## Information Processing
+
+### English Sources
+
+For English materials, I initially used "Youdao Dictionary" for word translation, Google Translate for sentences, and "Deepl" for paragraphs. Eventually, I realized this was too slow and inefficient. Ideally, a single tool that can handle word, sentence, and paragraph translation would be optimal. After researching, I chose "Quicker" + "Saladict" for translation.
+
+![](https://cdn.sspai.com/2022/10/11/a7ebb1d3c46702b56bd6d171dfcfc075.png)
+
+This combo allows translation outside browsers and supports words, sentences, and paragraphs, offering results from multiple translation platforms. For non-urgent word lookups, the "Collins Advanced" dictionary is helpful as it explains English words in English, providing context to aid understanding.
+
+![](https://cdn.sspai.com/2022/10/11/article/827c9a8048c83e504ccb15893702bf09)
+
+### Multimedia Information
+
+After processing text-based information, it's important to consider how to handle multimedia information. Specifically, I'm referring to English videos, as I don't have a habit of learning through podcasts or recordings and I rarely watch Chinese tutorials anymore. Many renowned universities offer open courses in video format. Wouldn't it be helpful if you could take notes on these videos? Have you ever thought it would be great if you could convert the content of a lecture into text, since we usually read faster than a lecturer speaks? Fortunately, the software `Language Reactor` can export subtitles from YouTube and Netflix videos, along with Chinese translations.
+
+We can copy the subtitles exported by `Language Reactor` into `Obsidian` and read them as articles. Besides learning purposes, you can also use this plugin while watching YouTube videos. It displays subtitles in both English and Chinese, and you can click on unfamiliar words in the subtitles to see their definitions.
+
+![](https://cdn.sspai.com/2022/10/11/364c8e6ed263affa84d9eee61338b4af.png)
+
+However, reading texts isn't always the most efficient way to learn about some abstract concepts. As the saying goes, "A picture is worth a thousand words." What if we could link a segment of text to corresponding images or even video operations? While browsing the `Obsidian` plugin marketplace, I discovered a plugin called `Media Extended`. This plugin allows you to add links in your notes that jump to specific times in a video, effectively connecting your notes to the video! This works well with the video subtitles mentioned earlier, where each line of subtitles corresponds to a time stamp, allowing for jumps to specific parts of the video. This means you don't have to cut specific video segments; instead, you can jump directly within the article!
+
+![](https://cdn.sspai.com/2022/10/11/17554cfdf662d5719ada453674012fdb.gif)
+
+`Obsidian` also has a powerful plugin called `Annotator`, which allows you to jump from notes to the corresponding section in a PDF.
+
+![](https://cdn.sspai.com/2022/10/11/article/b56994bf9a306830d8b0b8112677d3ec)
+
+Now, with `Obsidian`'s built-in double-chain feature, we can achieve inter-note linking, and with the above plugins, we can extend these links to multimedia. This completes the process of information handling. Learning often involves both a challenging ascent and a familiar descent. So, how can we incorporate the review process into this workflow?
+
+## Information Review
+
+`Obsidian` already has a plugin that connects to `Anki`, the renowned spaced repetition-based memory software. With this plugin, you can export segments of your notes to `Anki` as flashcards, each containing a link back to the original note.
+
+![](https://cdn.sspai.com/2022/10/11/1f7cebd8dd28f664d77cbf0ab228c406.gif)
+
+## Conclusion
+
+This workflow evolved over two years of learning in my spare time. Frustration with repetitive processes led to specific needs, which were fortunately met by tools I discovered online. Don't force tools into your workflow just for the sake of satisfaction; life is short, so focus on what's truly important.
+
+By the way, this article discusses the evolution of the workflow. If you're interested in the details of how this workflow is implemented, I recommend reading the following articles in order after this one:
+
+1. [3000+ Hours Accumulated Learning Workflow](https://sspai.com/post/75969)
+2. [Advanced Techniques in Obsidian | Creating Notes that Link to Any File Format](https://juejin.cn/post/7145351315705577485)

docs/必学工具/workflow.md

@@ -12,7 +12,7 @@
 
 于是我就顺其自然地开始翻阅英文书籍。不得不说，英文书籍内容的质量整体是比中文书籍高的。后来随着学习的层层深入，以知识的时效性和完整性出发，我发现 `源代码` > `官方文档` > `英文书籍` > `英文博客` > `中文博客`，最后我得出了一张 `信息损失图`：
 
-![](https://cdn.sspai.com/2022/10/11/bf07c1965a2e5bdf3f00644737789e2e.png)
+![](https://s2.loli.net/2024/10/18/diZGmFHz8162u4I.png)
 
 虽然一手信息很重要，但后面的 N 手信息并非一无是处，因为这 N 手资料里包含了作者对源知识的转化——例如基于某种逻辑的梳理（流程图、思维导图等）或是一些自己的理解（对源知识的抽象、类比、延伸到其他知识点），这些转化可以帮助我们更快地掌握和巩固知识的核心内容，就如同初高中学习时使用的辅导书。 此外，学习的过程中和别人的交流十分重要，这些 N 手信息同时起了和其他作者交流的作用，让我们能采百家之长。所以这提示我们学习一个知识点时先尽量选择质量更高的，信息损失较少的信息源，同时不妨参考多个信息源，让自己的理解更加全面准确。
 
@@ -26,7 +26,7 @@
 
 *   Obsidian 基于本地，打开速度快，且可存放很多电子书。我的笔记本是 32g 内存的华硕天选一代，拿来跑 Obsidian 可以快到飞起
 *   Obsidian 基于 Markdown。这也是一个优势，如果笔记软件写的笔记格式是自家的编码格式，那么不方便其他第三方拓展，也不方便将笔记用其他软件打开，比如 qq 音乐下载歌曲有自己的格式，其他播放器播放不了，这挺恶心人的
-*   Obsidian 有丰富的插件生态，并且这个生态既大又活跃，即插件数量多，且热门插件的 star 多，开发者会反馈用户 issue，版本会持续迭代。借助这些插件，可以使 Osidian 达到 `all in one` 的效果，即各类知识来源可以统一整合于一处
+*   Obsidian 有丰富的插件生态，并且这个生态既大又活跃，即插件数量多，且热门插件的 star 多，开发者会反馈用户 issue，版本会持续迭代。借助这些插件，可以使 Obsidian 达到 `all in one` 的效果，即各类知识来源可以统一整合于一处
 
 ## 信息的来源
 
@@ -35,13 +35,13 @@ Obsidian 的插件使其可以支持 pdf 格式，而其本身又支持 Markdown
 *   有什么格式
 *   怎么转换为 pdf 或 Markdown
 
-![](https://cdn.sspai.com/2022/10/11/3801b1c9b94286566fe677e3b12cc7b0.png)
+![](https://s2.loli.net/2024/10/18/JhDUR5tgNdO94mo.png)
 
 ### 有什么格式
 
 文件格式依托于其展示的平台，所以在看有什么格式之前，可以罗列一下我平时获取信息的来源：
 
-![](https://cdn.sspai.com/2022/10/11/07e97f372850054958d4961a3787a93f.png)
+![](https://s2.loli.net/2024/10/18/TycU8l71s9BJSVI.png)
 
 
 可以看到主要分为`文章`、`论文`、`电子书`、`课程`四类，包含的格式主要有 `网页`、`pdf` 、`mobi`、`azw`、`azw3`。
@@ -50,27 +50,27 @@ Obsidian 的插件使其可以支持 pdf 格式，而其本身又支持 Markdown
 
 在线的文章和课程等大多以网页形式呈现，而将网页转换为 Markdown 可以使用剪藏软件，它可以将网页文章转换为多种文本格式文件。我选择的工具是简悦，使用简悦可以将几乎所有平台的文章很好地剪藏为 Markdown 并且导入到 Obsidian。
 
-![](https://cdn.sspai.com/2022/10/11/211cffa78f20a9e7286a7419e9e0b878.png)
+![](https://s2.loli.net/2024/10/18/S5hcofUlv1x9dX3.png)
 
 对于论文和电子书而言如果格式本身就是 pdf 则万事大吉，但如果是其他格式则可以使用 calibre 进行转换：
 
-![](https://cdn.sspai.com/2022/10/11/51575f65f6f4c6edfa6c5b97fd16d625.png)
+![](https://s2.loli.net/2024/10/18/OwrYoqxCjRgFJpZ.png)
 
 现在利用 Obsidian 的 pdf 插件和其原生的 markdown 支持就可以畅快无比地做笔记并且在这些文章的对应章节进行无缝衔接地引用跳转啦（具体操作参考下文的“信息的处理”模块）。
 
-![](https://cdn.sspai.com/2022/10/11/d64a9a2d6406d2d367dcb505ede69c83.png)
+![](https://s2.loli.net/2024/10/18/7eEWwftYC3KIjik.png)
 
 ### 如何统一管理信息来源
 
 对于 pdf 等文件类资源可以本地或者云端存储，而网页类资源则可以分门别类地放入浏览器的收藏夹，或者剪藏成 markdown 格式的笔记，但是网页浏览器不能实现移动端的网页收藏。为了实现跨端网页收藏我选用了 Cubox，在手机端看到感兴趣的网页时只需小手一划，便能将网页统一保存下来。虽然免费版只能收藏 100 个网页，但其实够用了，还可以在收藏满时督促自己赶紧剪藏消化掉这些网页，让收藏不吃灰。
 
-![](https://cdn.sspai.com/2022/10/11/ad7ebfcb4619f64a41d328b88e0e3a12.png)
+![](https://s2.loli.net/2024/10/18/HU4kO6ofexS2lQM.png)
 
 除此之外，回想一下我们平时收藏的网页，就会发现有很多并不是像知乎、掘金这类有完整功能的博客平台，更多的是个人建的小站，而这些小站往往没有移动端应用，这样平时刷手机的时候也看不到，放到浏览器的收藏夹里又容易漏了看，有新文章发布我们也不能第一时间收到通知，这个时候就需要一种叫 `RSS` 的通信协议。  
 
 `RSS`（英文全称：RDF Site Summary 或 Really Simple Syndication），中文译作简易信息聚合，也称聚合内容，是一种消息来源格式规范，用以聚合多个网站更新的内容并自动通知网站订阅者。电脑端可以借助 `RSSHub Radar` 来快速发现和生成 `RSS` 订阅源，接着使用 `Feedly` 来订阅这些 `RSS` 订阅源（`RSSHub Radar` 和 `Feedly` 在 chrome 浏览器中均有官方插件）。
 
-![](https://cdn.sspai.com/2022/10/11/5df6cd9d967f190df35928e781f9185f.png)
+![](https://s2.loli.net/2024/10/18/5qAKwzYEgmb821F.png)
 
 到这里为止，收集信息的流程已经比较完备了。但资料再多，分类规整得再漂亮，也得真正内化成自己的才管用。因此在收集完信息后就得进一步地处理信息，即阅读这些信息，如果是英文信息的话还得搞懂英文的语义，加粗高亮重点句子段落，标记有疑问的地方，发散联想相关的知识点，最后写上自己的总结。那么在这过程中需要使用到什么工具呢？
 
@@ -80,11 +80,11 @@ Obsidian 的插件使其可以支持 pdf 格式，而其本身又支持 Markdown
 
 面对英文的资料，我以前是用 `有道词典` 来划词翻译，遇到句子的话就使用谷歌翻译，遇到大段落时就使用 `deepl`，久而久之，发现这样看英语文献太慢了，得用三个工具才能满足翻译这一个需求，如果有一个工具能够同时实现对单词、句子和段落的划词翻译就好了。我联想到研究生们应该会经常接触英语文献，于是我就搜 `研究生` + `翻译软件`，在检索结果里我最终选择了 `Quicker` + `沙拉查词` 这个搭配来进行划词翻译。
 
-![](https://cdn.sspai.com/2022/10/11/a7ebb1d3c46702b56bd6d171dfcfc075.png)
+![](https://s2.loli.net/2024/10/18/odmKinLV3hybCOa.png)
 
 使用这套组合可以实现在浏览器外的其他软件内进行划词翻译，并且支持单词、句子和段落的翻译，以及每次的翻译会有多个翻译平台的结果。btw，如果查单词时不着急的话，可以顺便看看 `科林斯高阶` 的翻译，这个词典的优点就是会用英文去解释英文，可以提供多个上下文帮助你理解，对于学习英文单词也有帮助，因为用英文解释英文才更接近英语的思维。
 
-![](https://cdn.sspai.com/2022/10/11/article/827c9a8048c83e504ccb15893702bf09)
+![](https://s2.loli.net/2024/10/18/ZtG9XsoPde5HQBn.png)
 
 ### 多媒体信息
 
@@ -92,15 +92,15 @@ Obsidian 的插件使其可以支持 pdf 格式，而其本身又支持 Markdown
 
 我们可以把 `Language Reactor` 导出的字幕复制到 `Obsidian` 里面作为文章来读。除了出于学习的需求，也可以在平时看油管的视频时打开这个插件，这个插件可以同时显示中英文字幕，并且可以单击选中英文字幕中你认为生僻的单词后显示单词释义。
 
-![](https://cdn.sspai.com/2022/10/11/364c8e6ed263affa84d9eee61338b4af.png)
+![](https://s2.loli.net/2024/10/18/osDmqFvLtPVcidh.png)
 
 但阅读文本对于一些抽象的知识点来说并不是效率最高的学习方式。俗话说，一图胜千言，能不能将某一段知识点的文本和对应的图片甚至视频画面操作联系起来呢？我在浏览 `Obsidian` 的插件市场时，发现了一个叫 `Media Extended` 的插件，这个插件可以在你的笔记里添加跳转到视频指定时间进度的链接，相当于把你的笔记和视频连接起来了！这刚好可以和我上文提到的生成视频中英文字幕搭配起来，即每一句字幕对应一个时间，并且能根据时间点跳转到视频的指定进度，如此一来如果需要在文章中展示记录了操作过程的视频的话，就不需要自己去截取对应的视频片段，而是直接在文章内就能跳转！
 
-![](https://cdn.sspai.com/2022/10/11/17554cfdf662d5719ada453674012fdb.gif)
+![](https://s2.loli.net/2024/10/18/LPwz8AKEfxuIMYS.gif)
 
 `Obsidian` 里还有一个很强大的插件，叫 `Annotator`，它可以实现笔记内跳转到 pdf 原文
 
-![](https://cdn.sspai.com/2022/10/11/article/b56994bf9a306830d8b0b8112677d3ec)
+![](https://s2.loli.net/2024/10/18/dokCZEzrjl7AcI9.gif)
 
 现在，使用 `Obsidian` 自带的双链功能，可以实现笔记间相互跳转，结合上述两个插件，可以实现笔记到多媒体的跳转，信息的处理过程已经完备。一般我们学习的过程相当于上山和下山，刚学的时候就好像上山，很陌生、吃力，所谓学而时习之，复习或练习的过程就像下山，没有陌生感，不见得轻松，但非走不可。那么如何把复习这一过程纳入工作流的环节里呢？
 
@@ -108,7 +108,7 @@ Obsidian 的插件使其可以支持 pdf 格式，而其本身又支持 Markdown
 
 `Obsidian` 内已经有一个连接 `Anki` 的插件，`Anki` 就是大名鼎鼎的、基于间隔重复的记忆软件。使用该插件可以截取笔记的片段导出到 `Anki` 并变成一张卡片，卡片内也有跳转回笔记原文的链接
 
-![](https://cdn.sspai.com/2022/10/11/1f7cebd8dd28f664d77cbf0ab228c406.gif)
+![](https://s2.loli.net/2024/10/18/ivexghT64HIYPJq.gif)
 
 ## 总结
 
@@ -117,4 +117,4 @@ Obsidian 的插件使其可以支持 pdf 格式，而其本身又支持 Markdown
 btw，此篇文章是讲解工作流的演化思路，如果对此工作流的实现细节感兴趣，建议阅读完本文后再按顺序阅读以下文章
 
 1.  [3000 + 小时积累的学习工作流](https://sspai.com/post/75969)
-2.  [Obsidian 的高级玩法 | 打造能跳转到任何格式文件的笔记](https://juejin.cn/post/7145351315705577485)
+2.  [Obsidian 的高级玩法 | 打造能跳转到任何格式文件的笔记](https://juejin.cn/post/7145351315705577485)

docs/必学工具/信息检索.en.md

@@ -0,0 +1,99 @@
+# Information Retrieval
+
+## Introduction
+
+> When encountering a problem, remember the first thing is to **read the documentation**. Don't start by searching online or asking others directly. Reviewing FAQs may quickly provide the answer.
+
+Information retrieval, as I understand it, is essentially about skillfully using search engines to quickly find the information you need, including but not limited to programming.
+
+The most important thing in programming is STFW (search the fucking web) and RTFM (read the fucking manual). First, you should read the documentation, and second, learn to search. With so many resources online, how you use them depends on your information retrieval skills.
+
+To understand how to search effectively, we first need to understand how search engines work.
+
+## How Search Engines Work
+
+The working process of a search engine can generally be divided into three stages: [^1]
+
+1. Crawling and Fetching: Search engine spiders visit web pages by tracking links, obtain the HTML code of the pages, and store it in a database.
+2. Preprocessing: The indexing program processes the fetched web page data by extracting text, segmenting Chinese words, indexing, etc., preparing for the ranking program.
+3. Ranking: When users enter keywords, the ranking program uses the indexed data to calculate relevance and then generates the search results page in a specific format.
+
+The first step involves web crawlers, often exaggerated in Python courses. It can be simply understood as using an automated program to download all text, images, and related information from websites and store them locally.
+
+The second step is the core of a search engine, but not critical for users to understand. It can be roughly understood as cleaning data and indexing pages, each with keywords for easy querying.
+
+The third step is closely related to us. Whether it's Google, Baidu, Bing, or others, you input keywords or queries, and the search engine returns results. This article teaches you how to obtain better results.
+
+## Basic Search Techniques
+
+Based on the above working principles, we can roughly understand that a search engine can be treated as a smart database. Using better query conditions can help you find the information you need faster. Here are some search techniques:
+
+### Use English
+
+First, it's important to know that in programming, it's best to search in English. Reasons include:
+
+1. In programming and various software operations, English resources are of higher quality than those in Chinese or other languages.
+2. Due to translation issues, English terms are more accurate and universally applicable than Chinese.
+3. Chinese search engines' word segmentation systems can lead to ambiguity. For example, Google searches in Chinese may not yield many useful results.
+
+If your English is not strong, use translation tools like Baidu or Sogou; they are sufficient.
+
+### Refine Keywords
+
+Don't search whole sentences. Although search engines automatically segment words, searching with whole sentences versus keywords can yield significantly different results in accuracy and order. Search engines are machines, not your teachers or colleagues. As mentioned above, searching is actually querying a database crawled by the search engine, so it's better to break down into keywords or phrases.
+
+For example, if you want to know how to integrate vcpkg into a project instead of globally, searching for "如何将vcpkg集成到项目中而不是全局" in a long sentence may not yield relevant results. It's better to break it down into keywords like "vcpkg 集成 项目 全局".
+
+### Replace Keywords
+
+If you can't find what you're looking for, try replacing "项目" with "工程" or remove "集成". If that doesn't work, try advanced searching.
+
+### Advanced Searching
+
+Most search engines support advanced searching, including Google, Bing, Baidu, Ecosia, etc. Common formats include:
+
+* Exact Match: Enclose the search term in quotes for precise matching.
+* Exclude Keywords: Use a minus sign (-) to exclude specific words.
+* Include Keywords: Use a plus sign (+) to ensure a keyword is included.
+* Search Specific File Types: Use `filetype:pdf` to search for PDF files directly.
+* Search Specific Websites: Use `site:stackoverflow.com` to search within a specific site.
+
+Refer to the website instructions for specific syntax, such as [Baidu Advanced Search](https://baike.baidu.com/item/高级搜索/1743887?fr=aladdin) or [Bing Advanced Search Keywords](https://help.bing.microsoft.com/#apex/bing/zh-CHS/10001/-1).
+
+#### GitHub Advanced Search
+
+Use [GitHub's Advanced Search page](https://github.com/search/advanced) or refer to [GitHub Query Syntax](https://zhuanlan.zhihu.com/p/273766377) for advanced searches on GitHub. Examples include searching by repository name, description, readme, stars, fork count, size, update/creation date, license, language, user, and organization. These can be
+
+ used in combination.
+
+### More Tips
+
+Depending on the context, I recommend specific sites for certain queries:
+
+* For language-specific queries (e.g., C++/Qt/OpenGL), add `site:stackoverflow.com`.
+* For specific business/development environments or software-related issues, first check BugLists, IssueLists, or relevant forums.
+* QQ groups are also a place to ask questions, but make sure your queries are meaningful.
+* Chinese platforms like Zhihu, Jian Shu, Blog Park, and CSDN have a wealth of Chinese notes and experiences.
+
+### About Baidu
+
+Many programmers advise against using Baidu, preferring Google or Bing International. However, if you really need it, consider using alternatives like Ecosia or Yandex. For Chinese searches, Baidu might actually be the best option due to its database and indexing policies.
+
+## Code Search
+
+In addition to search engines, you might also need to search for code, either your own or from projects. Here are some recommended tools:
+
+### Local Code Search
+
+* ACK or ACK2, well-established search tools written in Perl.
+* The Silver Searcher, implemented in C.
+* The Platinum Searcher, implemented in Go.
+* FreeCommander's built-in search, efficient on solid-state drives.
+* IDE's built-in search, though not always the most user-friendly.
+
+### Open Source Code Search
+
+* [Searchcode](https://searchcode.com) for searching open source code, known for speed.
+* [一行代码](https://www.alinecode.com) a useful Chinese tool for code search.
+
+[^1]: [Introduction to How Search Engines Work - Zhihu](https://zhuanlan.zhihu.com/p/301641935)

docs/必学工具/翻墙.en.md

@@ -0,0 +1,3 @@
+# GFW
+
+[This link](https://wallesspku.space/) appears here purely as a random combination of binary bits and has nothing to do with me.

docs/操作系统/CS162.en.md

@@ -18,7 +18,7 @@ Unlike the small but comprehensive design philosophy in MIT's xv6 labs, *Pintos*
 
 Although it is tough, Stanford, Berkeley, JHU and many other top U.S. colleges have chosen *Pintos* as their OS course project. If you're really interested in operating systems, it will greatly improve your ability to write and debug low-level system code. For me, it is an invaluable experience to design, implement, and debug a large system independently.
 
-*Pintos* will also be introduced as a course project in Peking University's OS Course. In the Spring 2022 semester, I worked with [another TA](https://github.com/AlfredThiel) to write a comprehensive [lab documentation](https://alfredthiel.gitbook.io/pintosbook/) and provided a docker image for the ease of cross-platform development. In the last semester before graduation, I hope such an attempt can make more people fall in love with systems and contribute to the field of systems in China.
+*Pintos* will also be introduced as a course project in Peking University's OS Course. In the Spring 2022 semester, I worked with [another TA](https://github.com/AlfredThiel) to write a comprehensive [lab documentation](https://pkuflyingpig.gitbook.io/pintos) and provided a docker image for the ease of cross-platform development. In the last semester before graduation, I hope such an attempt can make more people fall in love with systems and contribute to the field of systems in China.
 
 ## Course Resources
 

docs/操作系统/CS162.md

@@ -20,7 +20,7 @@
 
 虽然难度很大，但 Stanford, Berkeley, JHU 等多所美国顶尖名校的操统课程均采用了 Pintos。如果你真的对操作系统很感兴趣，Pintos 会极大地提高你编写和 debug 底层系统代码的能力。在本科阶段，能自己设计、实现并 debug 一个大型系统，是一段非常珍贵的经历。
 
-北大 2022 年春季学期的操作系统实验班也将会首次引入 Pintos 作为课程 Project。我和该课程的[另一位助教](https://github.com/AlfredThiel)整理并完善了 Pintos 的[实验文档](https://alfredthiel.gitbook.io/pintosbook/)，并利用 Docker 配置了跨平台的实验环境，想自学的同学可以按文档自行学习。在毕业前的最后一个学期，希望能用这样的尝试，让更多人爱上系统领域，为国内的系统研究添砖加瓦。
+北大 2022 年春季学期的操作系统实验班也将会首次引入 Pintos 作为课程 Project。我和该课程的[另一位助教](https://github.com/AlfredThiel)整理并完善了 Pintos 的[实验文档](https://pkuflyingpig.gitbook.io/pintos)，并利用 Docker 配置了跨平台的实验环境，想自学的同学可以按文档自行学习。在毕业前的最后一个学期，希望能用这样的尝试，让更多人爱上系统领域，为国内的系统研究添砖加瓦。
 
 ## 课程资源
 

docs/操作系统/MIT6.S081.en.md

@@ -31,6 +31,8 @@ In the second half of the course, the instructors will discuss a couple of class
 - [Detailed Explanation of xv6](https://space.bilibili.com/1040264970/)
 - [xv6 Documentation(Chinese)](https://th0ar.gitbooks.io/xv6-chinese/content/index.html)
 - [line-by-line walk-through of key xv6 source codes](https://www.youtube.com/playlist?list=PLbtzT1TYeoMhTPzyTZboW_j7TPAnjv9XB)
+- [Text Book Translation xv6-riscv-book-zh-cn](https://blog.betteryuan.top/archives/xv6-riscv-book-zh-cn)
+- [Text Book Translation SRC xv6-riscv-book-zh-cn](https://github.com/HelloYJohn/xv6-riscv-book-zh-cn.git)
 
 ## Complementary Resources
 
@@ -46,7 +48,7 @@ All resources used and assignments implemented by @PKUFlyingPig when learning th
 ### Some Blogs for References
 
 - [doraemonzzz](http://doraemonzzz.com/tags/6-S081/)
-- [Xiao Fan (樊潇)](https://fanxiao.tech/posts/MIT-6S081-notes/)
+- [Xiao Fan (樊潇)](https://fanxiao.tech/posts/2021-03-02-mit-6s081-notes/)
 - [Miigon's blog](https://blog.miigon.net/categories/mit6-s081/)
 - [Zhou Fang](https://walkerzf.github.io/categories/6-S081/index.html)
 - [Yichun's Blog](https://www.yichuny.page/tags/Operating%20System)

docs/操作系统/MIT6.S081.md

@@ -31,6 +31,8 @@
 - [xv6 操作系统的深入讲解](https://space.bilibili.com/1040264970/)
 - [xv6 中文文档](https://th0ar.gitbooks.io/xv6-chinese/content/index.html)
 - [xv6 关键源码逐行解读 + 整体架构分析](https://www.youtube.com/playlist?list=PLbtzT1TYeoMhTPzyTZboW_j7TPAnjv9XB)
+- [课程教材翻译 xv6-riscv-book-zh-cn](https://blog.betteryuan.top/archives/xv6-riscv-book-zh-cn)
+- [课程教材翻译源码 xv6-riscv-book-zh-cn](https://github.com/HelloYJohn/xv6-riscv-book-zh-cn.git)
 
 ## 资源汇总
 
@@ -46,10 +48,11 @@
 ### 一些可以参考的博客
 
 - [doraemonzzz](http://doraemonzzz.com/tags/6-S081/)
-- [Xiao Fan (樊潇)](https://fanxiao.tech/posts/MIT-6S081-notes/)
+- [Xiao Fan (樊潇)](https://fanxiao.tech/posts/2021-03-02-mit-6s081-notes/)
 - [Miigon's blog](https://blog.miigon.net/categories/mit6-s081/)
 - [Zhou Fang](https://walkerzf.github.io/categories/6-S081/index.html)
 - [Yichun's Blog](https://www.yichuny.page/tags/Operating%20System)
 - [解析Ta](https://blog.csdn.net/u013577996/article/details/108679997)
 - [PKUFlyingPig](https://github.com/PKUFlyingPig/MIT6.S081-2020fall)
 - [星遥见](https://www.cnblogs.com/weijunji/tag/XV6/)
+- [tzyt 的博客](https://ttzytt.com/tags/xv6/)

docs/操作系统/NJUOS.en.md

@@ -0,0 +1,30 @@
+# NJU OS: Operating System Design and Implementation
+
+## Course Introduction
+
+- **University**: Nanjing University
+- **Prerequisites**: Computer Architecture + Solid C programming skills
+- **Programming Language**: C
+- **Course Difficulty**: 🌟🌟🌟🌟
+- **Estimated Study Time**: 150 hours
+
+I had always heard that the operating system course taught by Professor Yanyan Jiang at Nanjing University was excellent. This semester, I had the opportunity to watch his lectures on Bilibili and gained a lot. As a young professor with rich coding experience, his teaching is full of a hacker's spirit. Often in class, he would start coding in the command line on a whim, and many important points were illustrated with vivid and straightforward code examples. What struck me most was when he implemented a mini executable file and a series of binary tools to help students better understand the design philosophy of dynamic link libraries, solving many problems that had puzzled me for years.
+
+In the course, Prof. Jiang starts from the perspective that "programs are state machines" to establish an explainable model for the "root of all evil" concurrent programs. Based on this, he discusses common methods of concurrency control and strategies for dealing with concurrency bugs. Then, he views the operating system as a series of objects (processes/threads, address spaces, files, devices, etc.) and their APIs (system calls), combined with rich practical examples to show how operating systems use these objects to virtualize hardware resources and provide various services to application software. In the final part about persistence, he builds up various storage devices from 1-bit storage media and abstracts a set of interfaces through device drivers to facilitate the design and implementation of file systems. Although I have taken many operating system courses before, this unique approach has given me many unique perspectives on system software.
+
+In addition to its innovative theoretical instruction, the course's emphasis on practice is a key feature of Prof. Jiang's teaching. In class and through programming assignments, he subtly cultivates the ability to read source code and consult manuals, which are essential skills for computer professionals. During the fifth MiniLab, I read Microsoft's FAT file system manual in detail for the first time, gaining a very valuable experience.
+
+The programming assignments consist of 5 MiniLabs and 4 OSLabs. Unfortunately, the grading system is only open to students at Nanjing University. However, Professor Jiang generously allowed me to participate after I emailed him. I completed the 5 MiniLabs, and the overall experience was excellent. Particularly, the second coroutine experiment left a deep impression on me, where I experienced the beauty and "terror" of context switching in a small experiment of less than a hundred lines. Also, the MiniLabs can be easily tested locally, so the lack of a grading system should not hinder self-learning. Therefore, I hope others will not collectively "harass" the professor for access.
+
+Finally, I want to thank Professor Jiang again for designing and offering such an excellent operating system course, the first independently developed computer course from a domestic university included in this book. It's thanks to young, new-generation teachers like Professor Jiang, who teach with passion despite the heavy Tenure track evaluation, that many students have an unforgettable undergraduate experience. I also look forward to more such high-quality courses in China, which I will include in this book for the benefit of more people.
+
+## Course Resources
+
+- Course Website: <https://jyywiki.cn/OS/2022/index.html>
+- Course Videos: <https://space.bilibili.com/202224425/channel/collectiondetail?sid=192498>
+- Course Textbook: <http://pages.cs.wisc.edu/~remzi/OSTEP/>
+- Course Assignments: <https://jyywiki.cn/OS/2022/index.html>
+
+## Resource Summary
+
+As per Professor Jiang's request, my assignment implementations are not open-sourced.

docs/操作系统/NJUOS.md

@@ -20,10 +20,10 @@
 
 ## 课程资源
 
-- 课程网站：<http://jyywiki.cn/OS/2022/>
+- 课程网站：<https://jyywiki.cn/OS/2022/index.html>
 - 课程视频：<https://space.bilibili.com/202224425/channel/collectiondetail?sid=192498>
 - 课程教材：<http://pages.cs.wisc.edu/~remzi/OSTEP/>
-- 课程作业：<http://jyywiki.cn/OS/2022/>
+- 课程作业：<https://jyywiki.cn/OS/2022/index.html>
 
 ## 资源汇总
 

docs/数学基础/MITLA.en.md

@@ -16,7 +16,9 @@ In addition to the course materials, the famous Youtuber **3Blue1Brown**'s video
 
 ## Resources
 
-- Course Website: <https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/syllabus/>
+- Course Website: [fall2011](https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/syllabus/)
 - Recordings: refer to the course website
 - Textbook: Introduction to Linear Algebra, Gilbert Strang
 - Assignments: refer to the course website
+
+On May 15th, 2023, revered mathematics professor Gilbert Strang capped his 61-year career as a faculty member at MIT by delivering his [final 18.06 Linear Algebra lecture](https://ocw.mit.edu/courses/18-06sc-linear-algebra-fall-2011/pages/final-1806-lecture-2023/) before retiring at the age of 88. In addition to a brief review for the course final exam, the overflowing audience (both in person and on the live YouTube stream) heard recollections, appreciations, and congratulations from Prof. Strang’s colleagues and former students. A rousing standing ovation concluded this historic event.

docs/数学基础/MITLA.md

@@ -10,13 +10,15 @@
 
 数学大牛 Gilbert Strang 老先生年逾古稀仍坚持授课，其经典教材 [Introduction to Linear Algebra](https://math.mit.edu/~gs/linearalgebra/) 已被清华采用为官方教材。我当时看完盗版 PDF 之后深感愧疚，含泪花了两百多买了一本英文正版收藏。下面附上此书封面，如果你能完全理解封面图的数学含义，那你对线性代数的理解一定会达到新的高度。
 
-![image](https://math.mit.edu/~gs/linearalgebra/ila5/linearalgebra5_Front.jpg)
+ <img src="https://math.mit.edu/~gs/linearalgebra/ila5/linearalgebra5_Front.jpg" width = "300" height = "300" align=center />
 
 配合油管数学网红 **3Blue1Brown** 的[线性代数的本质](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)系列视频食用更佳。
 
 ## 课程资源
 
-- 课程网站：<https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/syllabus/>
+- 课程网站：[fall2011](https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/syllabus/)
 - 课程视频：参见课程网站
 - 课程教材：Introduction to Linear Algebra. Gilbert Strang
 - 课程作业：参见课程网站
+
+2023年5月15日，Gilbert Strang 上完了他在 18.06 的[最后一课](https://ocw.mit.edu/courses/18-06sc-linear-algebra-fall-2011/pages/final-1806-lecture-2023/)，以88岁高龄结束了在其 MIT 61年的教学及科研生涯。但他的线性代数课已经并且还将继续影响一代代青年学子，让我们向老先生致以最崇高的敬意。

docs/数学基础/information.en.md

@@ -12,6 +12,6 @@ This is MIT's introductory information theory course for freshmen, Professor Pen
 
 ## 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>
+- Course Website: [spring2008](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/index.htm)
+- Textbook: [Information and Entropy](https://ocw.mit.edu/courses/6-050j-information-and-entropy-spring-2008/resources/mit6_050js08_textbook/)
 - Assignments: see the course website for details, including written assignments and Matlab programming assignments.

docs/数学基础/information.md

@@ -14,6 +14,6 @@ MIT 面向大一新生的信息论入门课程，Penfield 教授专门为这门
 
 ## 课程资源
 
-- 课程网站：<https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/index.htm>
-- 课程教材：<https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/syllabus/MIT6_050JS08_textbook.pdf>
+- 课程网站：[spring2008](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/index.htm)
+- 课程教材：[Information and Entropy](https://ocw.mit.edu/courses/6-050j-information-and-entropy-spring-2008/resources/mit6_050js08_textbook/)
 - 课程作业：详见课程网站，包含书面作业与 Matlab 编程作业。

docs/数学进阶/6.042J.en.md

@@ -12,6 +12,6 @@ This is MIT‘s discrete mathematics and probability course taught by the notabl
 
 ## 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/>
+- Course Website: [spring2015](https://ocw.mit.edu/courses/6-042j-mathematics-for-computer-science-spring-2015/), [fall2010](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/), [fall2005](https://ocw.mit.edu/courses/6-042j-mathematics-for-computer-science-fall-2005/)
+- Recordings: Refer to the course website
+- Assignments: Refer to the course website

docs/数学进阶/6.042J.md

@@ -12,6 +12,6 @@ MIT 的离散数学以及概率综合课程，导师是大名鼎鼎的 **Tom Lei
 
 ## 课程资源
 
-- 课程网站：<https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/>
-- 课程视频：<https://www.bilibili.com/video/BV1L741147VX>
-- 课程作业：<https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/assignments/>
+- 课程网站：[spring2015](https://ocw.mit.edu/courses/6-042j-mathematics-for-computer-science-spring-2015/), [fall2010](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/), [fall2005](https://ocw.mit.edu/courses/6-042j-mathematics-for-computer-science-fall-2005/)
+- 课程视频：[spring2015](https://www.bilibili.com/video/BV1n64y1i777/?spm_id_from=333.337.search-card.all.click&vd_source=a4d76d1247665a7e7bec15d15fd12349), [fall2010](https://www.bilibili.com/video/BV1L741147VX/?spm_id_from=333.337.search-card.all.click&vd_source=a4d76d1247665a7e7bec15d15fd12349)
+- 课程作业：参考课程网站

docs/数学进阶/numerical.en.md

@@ -17,7 +17,7 @@ The designers of this course have also written an open source textbook for this
 ## Course Resources
 
 - Course Website: <https://github.com/mitmath/18330>
-- Textbook: <https://fncbook.github.io/fnc/frontmatter.html>
+- Textbook: <https://fncbook.com>
 - Assignments: 10 problem sets
 
 ## Personal Resources

docs/数学进阶/numerical.md

@@ -17,7 +17,7 @@
 ## 课程资源
 
 - 课程网站：<https://github.com/mitmath/18330>
-- 课程教材：<https://fncbook.github.io/fnc/frontmatter.html>
+- 课程教材：<https://fncbook.com>
 - 课程作业：10 个 Julia 编程作业
 
 ## 资源汇总

docs/数据库系统/15445.en.md

@@ -8,27 +8,53 @@
 - Difficulty: 🌟🌟🌟🌟
 - Class Hour: 100 hours
 
-This is an introductory course on databases at CMU taught by Andy Pavlo, a great expert in the field of databases. It teaches the basic topics of database systems from the bottom up, covering storage management, indexing, query processing, concurrency control, transactions, and so on.
+As an introductory course to databases at CMU, this course is taught by Andy Pavlo, a leading figure in the database field (quoted as saying, "There are only two things I care about in this world, one is my wife, the second is the database").
 
-The course project [Bustub](https://github.com/cmu-db/bustub) is an educational database system developed at CMU by the course instructors. During the semester, you will modify this database to implement the functions mentioned above. Moreover, as a well-written project in C++, Bustub obeys many modern development best practices, such as code specification, unit testing, cmake module build, etc. So it can be an excellent open source project to study.
+This is a high-quality, resource-rich introductory course to Databases. 
+
+The faculty and the CMU Database Group behind the course have open-sourced all the corresponding infrastructure (Autograder, Discord) and course materials (Lectures, Notes, Homework), enabling any student who is willing to learn about databases to enjoy an experience almost equivalent to that of a CMU student.
+
+One of the highlights of this course is the relational database [Bustub](https://github.com/cmu-db/bustub), which was specifically developed by the CMU Database Group for teaching purposes. It requires you to modify various components of this database and implement their functionalities.
+
+Specifically, in 15-445, you will need to implement some key components in `Bustub`, a traditional disk-oriented relational database, through the progression of four Projects.
+
+These components include the Buffer Pool Manager (for memory management), B Plus Tree (storage engine), Query Executors & Query Optimizer (operators & optimizer), and Concurrency Control, corresponding to `Project #1` through `Project #4`.
+
+Worth mentioning is that, during the implementation process, students can compile `bustub-shell` through `shell.cpp` to observe in real-time whether their implemented components are correct. The feedback is very sufficient.
+
+Furthermore, as a medium-sized project written in C++, bustub covers many requirements such as program construction, code standards, unit testing, etc., making it an excellent open-source project for learning.
 
 ## Resources
 
-- Course Website: [Fall2019](https://15445.courses.cs.cmu.edu/fall2019/schedule.html), [Fall2020](https://15445.courses.cs.cmu.edu/fall2020/schedule.html), [Fall2021](https://15445.courses.cs.cmu.edu/fall2021/schedule.html)
-- Recording: refer to the course website.
+- Course Website: [Fall 2019](https://15445.courses.cs.cmu.edu/fall2019/schedule.html), [Fall 2020](https://15445.courses.cs.cmu.edu/fall2020/schedule.html), [Fall 2021](https://15445.courses.cs.cmu.edu/fall2021/schedule.html), [Fall 2022](https://15445.courses.cs.cmu.edu/fall2022/schedule.html), [Spring 2023](https://15445.courses.cs.cmu.edu/spring2023/schedule.html)
+- Recording: The course website is freely accessible, and the [Youtube Lectures](https://www.youtube.com/playlist?list=PLSE8ODhjZXjaKScG3l0nuOiDTTqpfnWFf) for Fall 2022 are fully open-source.
 - Textbook: Database System Concepts
-- Assignments: 4 Projects
+- Assignments: Five Projects and Five Homework
 
-In Fall 2019, the second Project is to implement a hash table, and the fourth Project is to implement Logging, Recovery, and Checkpoints in your database system.
+In Fall 2019, `Project #2` involved creating a hash index, and `Project #4` focused on logging and recovery.
 
-In Fall 2020, the second Project is to implement B+Tree dynamic index structure, and the fourth Project is to implement concurrency control.
+In Fall 2020, `Project #2` was centered on `B-trees`, while `Project #4` dealt with concurrency control.
 
-In Fall 2021, the first Project is to implement a buffer pool, the second Project is to implement a hash table, and the fourth Project is to implement concurrency control.
+In Fall 2021, `Project #1` required the creation of a buffer pool manager, `Project #2` involved a hash index, and `Project #4` focused on concurrency control.
 
-If you have more time you can try them all, and when you do not understand the textbook very well, trying to implement the real system will deepen your understanding.
+In Fall 2022, the curriculum was similar to that of Fall 2021, with the only change being that the hash index was replaced by a B+ tree index, and everything else remained the same.
+
+In Spring 2023, the overall content was largely identical to Fall 2022 (buffer pool, B+ tree index, operators, concurrency control), except `Project #0` shifted to `Copy-On-Write Trie`. Additionally, a fun task of registering uppercase and lowercase functions was introduced, which allows you to see the actual effects of the functions you write directly in the compiled `bustub-shell`, providing a great sense of achievement.
+
+It's important to note that the versions of bustub prior to 2020 are no longer maintained. 
+
+The last `Logging & Recovery` Project in Fall 2019 is broken (it may still run on the `git head` from 2019, but Gradescope doesn't provide a public version, so it is not recommended to work on it, it is sufficient to just review the code and handout). 
+
+Perhaps in the Fall 2023 version, the recovery features will be fixed, and there may also be an entirely new `Recovery Project`. Let's wait and see 🤪.
+
+If you have the energy, I highly recommend giving all of them a try, or if there's something in the book that you don't quite understand, attempting the corresponding project can deepen your understanding (I personally suggest completing all of them, as I believe it will definitely be beneficial).
 
 ## Personal Resources
 
+The unofficial [Discord](https://discord.com/invite/YF7dMCg) is a great platform for discussion. The chat history practically documents the challenges that other students have encountered. You can also raise your own questions or help answer others', which I believe will be a great reference.
+
+For a guidance to get through Spring 2023, you can refer to [this article](https://zhuanlan.zhihu.com/p/637960746) by [@xzhseh](https://github.com/xzhseh) on [Zhihu](https://www.zhihu.com/) (Note: Since the article is originally written in Chinese, you may need a translator to read it :) ). It covers all the tools you need to succeed, along with guides and, most importantly, pitfalls that I've encountered, seen, or stepped into during the process of doing the Project.
+
 All the resources and assignments used by [@ysj1173886760](https://github.com/ysj1173886760) in this course are maintained in [ysj1173886760/Learning:db - GitHub](https://github.com/ysj1173886760/Learning/tree/master/db).
 
 Due to Andy's request, the repository does not contain the source code for the project, only the solution for homework. In particular, for Homework1, [@ysj1173886760](https://github.com/ysj1173886760) wrote a shell script to help you evaluate your solution automatically.

docs/数据库系统/15445.md

@@ -3,33 +3,56 @@
 ## 课程简介
 
 - 所属大学：CMU
-- 先修要求：C++，数据结构与算法
+- 先修要求：C++，数据结构与算法，CMU 15-213 (A.K.A. CS:APP，这也是 CMU 内部对每年 Enroll 同学的先修要求)
 - 编程语言：C++
 - 课程难度：🌟🌟🌟🌟
 - 预计学时：100 小时
 
-作为 CMU 数据库的入门课，这门课由数据库领域的大牛 Andy Pavlo 讲授（“这个世界上我只在乎两件事，一是我的老婆，二就是数据库”）。15-445 会自底向上地教你数据库系统的基本组成部分：存储、索引、查询，以及并发事务控制。
-这门课的亮点在于 CMU db 专门为此课开发了一个教学用的关系型数据库 [bustub](https://github.com/cmu-db/bustub)，并要求你对这个数据库的组成部分进行修改，实现上述部件的功能。此外 bustub 作为一个 C++ 编写的中小型项目涵盖了程序构建、代码规范、单元测试等众多要求，可以作为一个优秀的开源项目学习。
+作为 CMU 数据库的入门课，这门课由数据库领域的大牛 Andy Pavlo 讲授（“这个世界上我只在乎两件事，一是我的老婆，二就是数据库”）。
+
+这是一门质量极高，资源极齐全的 Database 入门课，这门课的 Faculty 和背后的 CMU Database Group 将课程对应的基础设施 (Autograder, Discord) 和课程资料 (Lectures, Notes, Homework) 完全开源，让每一个愿意学习数据库的同学都可以享受到几乎等同于 CMU 本校学生的课程体验。
+
+这门课的亮点在于 CMU Database Group 专门为此课开发了一个教学用的关系型数据库 [bustub](https://github.com/cmu-db/bustub)，并要求你对这个数据库的组成部分进行修改，实现上述部件的功能。
+
+具体来说，在 15-445 中你需要在四个 Project 的推进中，实现一个面向磁盘的传统关系型数据库 Bustub 中的部分关键组件。
+
+包括 Buffer Pool Manager (内存管理), B Plus Tree (存储引擎), Query Executors & Query Optimizer (算子们 & 优化器), Concurrency Control (并发控制)，分别对应 `Project #1` 到 `Project #4`。
+
+值得一提的是，同学们在实现的过程中可以通过 `shell.cpp` 编译出 `bustub-shell` 来实时地观测自己实现部件的正确与否，正反馈非常足。
+
+此外 bustub 作为一个 C++ 编写的中小型项目涵盖了程序构建、代码规范、单元测试等众多要求，可以作为一个优秀的开源项目学习。
 
 ## 课程资源
 
-- 课程网站：[Fall2019](https://15445.courses.cs.cmu.edu/fall2019/schedule.html), [Fall2020](https://15445.courses.cs.cmu.edu/fall2020/schedule.html), [Fall2021](https://15445.courses.cs.cmu.edu/fall2021/schedule.html), [Fall2022](https://15445.courses.cs.cmu.edu/fall2022/schedule.html)
-- 课程视频：课程网站免费观看
+- 课程网站：[Fall 2019](https://15445.courses.cs.cmu.edu/fall2019/schedule.html), [Fall 2020](https://15445.courses.cs.cmu.edu/fall2020/schedule.html), [Fall 2021](https://15445.courses.cs.cmu.edu/fall2021/schedule.html), [Fall 2022](https://15445.courses.cs.cmu.edu/fall2022/schedule.html), [Spring 2023](https://15445.courses.cs.cmu.edu/spring2023/schedule.html)
+- 课程视频：课程网站免费观看, Fall 2022 的 [Youtube 全开源 Lectures](https://www.youtube.com/playlist?list=PLSE8ODhjZXjaKScG3l0nuOiDTTqpfnWFf)
 - 课程教材：Database System Concepts
-- 课程作业：4 个 Project
+- 课程作业：5 个 Project 和 5 个 Homework
 
-在 Fall2019 中，第二个 Project 是做哈希索引，第四个 Project 是做日志与恢复。
+在 Fall 2019 中，`Project #2` 是做哈希索引，`Project #4` 是做日志与恢复。
 
-在 Fall2020 中，第二个 Project 是做 B 树，第四个 Project 是做并发控制。
+在 Fall 2020 中，`Project #2` 是做 B 树，`Project #4` 是做并发控制。
 
-在 Fall2021 中，第一个 Project 是做缓存池管理，第二个 Project 是做哈希索引，第四个 Project 是做并发控制。
+在 Fall 2021 中，`Project #1` 是做缓存池管理，`Project #2` 是做哈希索引，`Project #4` 是做并发控制。
 
-在 Fall2022 中，与 Fall2021 相比只有哈希索引换成了 B+ 树索引，其余都一样。
+在 Fall 2022 中，与 Fall 2021 相比只有哈希索引换成了 B+ 树索引，其余都一样。
 
-如果大家有精力的话可以都去尝试一下，或者在对书中内容理解不是很透彻的时候，尝试用代码写一个会加深你的理解。
+在 Spring 2023 中，大体内容和 Fall 2022 一样（缓存池，B+ 树索引，算子，并发控制），只不过 `Project #0` 换成了 `Copy-On-Write Trie`，同时增加了很好玩的注册大小写函数的 Task，可以直接在编译出的 `bustub-shell` 中看到自己写的函数的实际效果，非常有成就感。
+
+值得注意的是，现在 bustub 在 2020 年以前的 version 都已经停止维护。
+
+Fall 2019 的最后一个 `Logging & Recovery` 的 Project 已经 broken 了（在19年的 `git head` 上也许还可以跑，但尽管如此 Gradescope 应该也没有提供公共的版本，所以并不推荐大家去做，只看看代码和 Handout 就可以了）。
+
+或许在 Fall 2023 的版本 Recovery 相关的功能会被修复，届时也可能有全新的 `Recovery Project`，让我们试目以待吧🤪
+
+如果大家有精力的话可以都去尝试一下，或者在对书中内容理解不是很透彻的时候，尝试做一做对应的 Project 会加深你的理解（个人建议还是要全部做完，相信一定对你有帮助）。
 
 ## 资源汇总
 
+非官方的 [Discord](https://discord.com/invite/YF7dMCg) 是一个很好的交流平台，过往的聊天记录几乎记载了其他同学踩过的坑，你也可以提出你的问题，或者帮忙解答别人的问题，相信这是一份很好的参考。
+
+关于 Spring 2023 的通关指南，可以参考 [@xzhseh](https://github.com/xzhseh) 的这篇[CMU 15-445/645 (Spring 2023) Database Systems 通关指北](https://zhuanlan.zhihu.com/p/637960746)，里面涵盖了全部你需要的通关道具，和通关方式建议，以及最重要的，我自己在做 Project 的过程中遇到的，看到的，和自己亲自踩过的坑。
+
 @ysj1173886760 在学习这门课中用到的所有资源和作业实现都汇总在 [ysj1173886760/Learning: db - GitHub](https://github.com/ysj1173886760/Learning/tree/master/db) 中。
 
 由于 Andy 的要求，仓库中没有 Project 的实现，只有 Homework 的 Solution。特别的，对于 Homework1，@ysj1173886760 还写了一个 Shell 脚本来帮大家执行自动判分。

docs/数据库系统/15799.en.md

@@ -0,0 +1,28 @@
+# CMU 15-799: Special Topics in Database Systems
+
+## Course Introduction
+
+- **University**: Carnegie Mellon University (CMU)
+- **Prerequisites**: CMU 15-445
+- **Programming Language**: C++
+- **Course Difficulty**: 🌟🌟🌟
+- **Estimated Study Time**: 80 hours
+
+This course has only been offered twice so far, in Fall 2013 and Spring 2022, and it discusses some cutting-edge topics in the field of databases. The Fall 2013 session covered topics like Streaming, Graph DB, NVM, etc., while the Spring 2022 session mainly focused on Self-Driving DBMS, with relevant papers provided.
+
+The tasks for the Spring 2022 version of the course included:
+
+1. **Task One**: Manual performance tuning based on `PostgreSQL`.
+2. **Task Two**: Improving the Self-Driving DBMS based on [NoisePage Pilot](https://github.com/cmu-db/noisepage-pilot), with no limitations on features.
+
+The teaching style is more akin to a seminar, with fewer programming assignments. This course can broaden the horizons for general students and may be particularly beneficial for those specializing in databases.
+
+## Course Resources
+
+- **Course Homepages**:
+  - [CMU15-799 - Special Topics in Database Systems (Fall 2013)](https://15799.courses.cs.cmu.edu/fall2013)
+  - [CMU15-799 - Special Topics: Self-Driving Database Management Systems (Spring 2022)](https://15799.courses.cs.cmu.edu/spring2022/)
+
+- **Course Videos**: Not available
+
+- **Course Assignments**: 2 Projects + 1 Group Project

docs/数据库系统/CS122.en.md

@@ -0,0 +1,41 @@
+# Caltech CS 122: Database System Implementation
+
+## Course Introduction
+
+- **University**: California Institute of Technology (Caltech)
+- **Prerequisites**: None
+- **Programming Language**: Java
+- **Course Difficulty**: 🌟🌟🌟🌟🌟
+- **Estimated Study Time**: 150 hours
+
+Caltech's course, unlike CMU15-445 which does not offer SQL layer functionality, focuses on the implementation at the SQL layer in its CS122 course labs. It covers various modules of a query optimizer, such as SQL parsing, translation, implementation of joins, statistics and cost estimation, subquery implementation, and the implementation of aggregations and group by operations. Additionally, there are experiments related to B+ trees and Write-Ahead Logging (WAL). This course is suitable for students who have completed the CMU15-445 course and are interested in query optimization.
+
+Below is an overview of the first three assignments or lab experiments of this course:
+
+### Assignment 1
+
+- Provide support for delete and update statements in NanoDB.
+- Add appropriate pin/unpin code to the Buffer Pool Manager.
+- Improve the performance of insert statements without excessively inflating the size of the database file.
+
+### Assignment 2
+
+- Implement a simple plan generator to convert various parsed SQL statements into executable plans.
+- Implement join plan nodes that support inner and outer joins using the nested-loop join algorithm.
+- Add unit tests to ensure the correct implementation of inner and outer joins.
+
+### Assignment 3
+
+- Complete the collection of table statistics.
+- Perform plan cost calculation for various plan nodes.
+- Calculate the selectivity of various predicates that may appear in the execution plan.
+- Update the tuple statistics of the plan nodes' outputs based on predicates.
+
+For the remaining Assignments and Challenges, please refer to the course description. It is recommended to use IDEA to open the project and Maven for building, keeping in mind the log-related configurations.
+
+## Course Resources
+
+- Course Website: <http://courses.cms.caltech.edu/cs122/>
+- Course Code: <https://gitlab.caltech.edu/cs122-19wi>
+- Course Textbook: None
+- Course Assignments: 7 Assignments + 2 Challenges

docs/数据库系统/CS346.en.md

@@ -0,0 +1,30 @@
+# Stanford CS 346: Database System Implementation
+
+## Course Introduction
+
+- **University**: Stanford
+- **Prerequisites**: None
+- **Programming Language**: C++
+- **Course Difficulty**: 🌟🌟🌟🌟🌟
+- **Estimated Study Time**: 150 hours
+
+RedBase, the project for CS346, involves the implementation of a simplified database system and is highly structured. The project can be divided into the following parts, which also correspond to the four labs that need to be completed:
+
+1. **The Record Management Component**: This involves the implementation of record management functionalities.
+
+2. **The Index Component**: Focuses on the management of B+ tree indexing.
+
+3. **The System Management Component**: Deals with DDL statements, command-line tools, data loading commands, and metadata management.
+
+4. **The Query Language Component**: In this part, students are required to implement the RQL Redbase Query Language, including select, insert, delete, and update statements.
+
+5. **Extension Component**: Beyond the basic components of a database system, students must implement an extension component, which could be a Blob type, network module, join algorithms, CBO optimizer, OLAP, transactions, etc.
+
+RedBase is an ideal follow-up project for students who have completed CMU 15-445 and wish to learn other components of a database system. Due to its manageable codebase, it allows for convenient expansion as needed. Furthermore, as it is entirely written in C++, it also serves as good practice for C++ programming skills.
+
+## Course Resources
+
+- Course Website: <https://web.stanford.edu/class/cs346/2015/>
+- Course Code: <https://github.com/junkumar/redbase.git>
+- Course Textbook: None
+- Course Assignments: 4 Projects + 1 Extension

docs/数据库系统/CS346.md

@@ -20,7 +20,7 @@ RedBase 是 cs346 的一个项目，实现了一个简易的数据库系统，
 
 5. Extension Component：除了上述数据库系统的基本功能组件，还需要实现一个扩展组件，可以是 Blob 类型、 网络模块、连接算法、CBO 优化器、OLAP、事务等。
 
-RedBase 适合在学完 CMU-15445 后继续学习数据库系统中的其他组件，因为其代码量不多，可以方便的根据需要扩展代码。同时代码完全由 C++ 编写，也可以用于练习 C++ 编程技巧。
+RedBase 适合在学完 CMU 15-445 后继续学习数据库系统中的其他组件，因为其代码量不多，可以方便的根据需要扩展代码。同时代码完全由 C++ 编写，也可以用于练习 C++ 编程技巧。
 
 ## 课程资源
 

docs/数据结构与算法/6.006.md

@@ -8,7 +8,7 @@
 - 课程难度：🌟🌟🌟🌟🌟
 - 预计学时：100h+
 
-MIT-EECS 系的瑰宝。授课老师之一是算法届的奇才 Erik Demaine. 相比较于斯坦福的 [CS106B/X](../编程入门/CS106B_CS106X.md)（基于 C++ 的数据结构与算法课程），该课程更侧重于算法方面的详细讲解。课程也覆盖了一些经典的数据结构，如 AVL 树等。个人感觉在讲解方面比 CS106B 更加详细，也弥补了 CS106B 在算法方面讲解的不足。适合在 CS106B 入门之后巩固算法知识。
+MIT-EECS 系的瑰宝。授课老师之一是算法届的奇才 Erik Demaine. 相比较于斯坦福的 [CS106B/X](../编程入门/cpp/CS106B_CS106X.md)（基于 C++ 的数据结构与算法课程），该课程更侧重于算法方面的详细讲解。课程也覆盖了一些经典的数据结构，如 AVL 树等。个人感觉在讲解方面比 CS106B 更加详细，也弥补了 CS106B 在算法方面讲解的不足。适合在 CS106B 入门之后巩固算法知识。
 
 不过该课程也是出了名的难，大家需要做好一定的心理准备。
 

docs/数据结构与算法/CS61B.en.md

@@ -20,7 +20,7 @@ In addition, there are 3 projects that give you the opportunity to be exposed to
 ## Resources
 ## Course Resources
 
-- Course Website: <https://sp18.datastructur.es/>
+- Course Website: [spring2024](https://sp24.datastructur.es/), [fall2023](https://fa23.datastructur.es/), [spring2023](https://sp23.datastructur.es/), [spring2018](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 Homework and 3 Projects. Please refer to the course website for specific requirements.
@@ -28,3 +28,5 @@ In addition, there are 3 projects that give you the opportunity to be exposed to
 ## Personal resources
 
 All resources and homework implementations used by @PKUFlyingPig in this course are summarized in [PKUFlyingPig/CS61B - GitHub](https://github.com/PKUFlyingPig/CS61B).
+
+All resources and homework implementations used by @InsideEmpire in this course are summarized in [InsideEmpire/CS61B-PathwayToSuccess - GitHub](https://github.com/InsideEmpire/CS61B-PathwayToSuccess.git).

docs/数据结构与算法/CS61B.md

@@ -20,11 +20,13 @@
 
 ## 课程资源
 
-- 课程网站：<https://sp18.datastructur.es/>
-- 课程视频：<https://sp18.datastructur.es/>，每节课的链接详见课程网站
+- 课程网站：[spring2024](https://sp24.datastructur.es/), [fall2023](https://fa23.datastructur.es/), [spring2023](https://sp23.datastructur.es/), [spring2018](https://sp18.datastructur.es/)
+- 课程视频：原版视频参见课程网站，B站有中文翻译搬运。
 - 课程教材：无
 - 课程作业：每年略有不同，18 年春季学期有 14 个 Lab，10 个 Homework以及 3 个 Project，具体要求详见课程网站。
 
 ## 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/CS61B - GitHub](https://github.com/PKUFlyingPig/CS61B) 中。  
+@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/CS61B - GitHub](https://github.com/PKUFlyingPig/CS61B) 中。
+  
+@InsideEmpire 在学习这门课中用到的所有资源和作业实现都汇总在 [InsideEmpire/CS61B-PathwayToSuccess - GitHub](https://github.com/InsideEmpire/CS61B-PathwayToSuccess.git) 中。  

docs/机器学习/CS229.md

@@ -12,7 +12,7 @@
 
 ## 课程资源
 
-- 课程网站：<http://cs229.stanford.edu/syllabus.html>
+- 课程网站：<http://cs229.stanford.edu>
 - 课程视频：<https://www.bilibili.com/video/BV1JE411w7Ub>
 - 课程教材：无，课程 notes 写得非常好
 - 课程作业：不对公众开放

docs/机器学习系统/AICS.en.md

@@ -0,0 +1,41 @@
+# Intelligent Computing Systems
+
+## Course Overview
+
+- University: University of Chinese Academy of Sciences
+- Prerequisites: Computer Architecture, Deep Learning
+- Programming Languages: Python, C++, BCL
+- Course Difficulty: 🌟🌟🌟
+- Estimated Hours: 100+ hours
+
+Intelligent computing systems serve as the backbone for global AI, producing billions of devices annually, including smartphones, servers, and wearables. Training professionals for these systems is critical for China's AI industry competitiveness. Understanding intelligent computing systems is vital for computer science students, shaping their core skills.
+
+Prof. Yunji Chen's course, taught in various universities, uses experiments to provide a holistic view of the AI tech stack. Covering deep learning frameworks, coding in low-level languages, and hardware design, the course fosters a systematic approach.
+
+Personally, completing experiments 2-5 enhanced my grasp of deep learning frameworks. The BCL language experiment in chapter five is reminiscent of CUDA for those familiar.
+
+I recommend the textbook for a comprehensive tech stack understanding. Deep learning-savvy students can start from chapter five to delve into deep learning framework internals.
+
+Inspired by the course, I developed a [simple deep learning framework](https://github.com/ysj1173886760/PyToy) and plan a tutorial. Written in Python, it's code-light, suitable for students with some foundation. Future plans include more operators and potential porting to C++ for balanced performance and efficiency.
+
+## Course Resources
+
+- Course Website：[Official Website](https://novel.ict.ac.cn/aics/)
+- Course Videos：[bilibili](https://space.bilibili.com/494117284)
+- Course Textbook："Intelligent Computing Systems" by Chen Yunji
+
+## Personal Resources
+
+### New Edition Experiments for 2024
+
+- The 2024 edition of the Intelligent Computing Systems lab has undergone extensive adjustments in the knowledge structure, experimental topics, and lab manuals, including comprehensive use of PyTorch instead of TensorFlow, and the addition of experiments related to large models.
+- As the new lab topics and manuals have not been updated on the Cambricon Forum, the following repository is provided to store the new versions of the Intelligent Computing Systems lab topics, manuals, and individual experiment answers:
+- The resources for the new edition will be updated following the course schedule of the UCAS Spring Semester 2024, with completion expected by June 2024.
+- 2024 New labs, manuals, and answers created by @Yuichi: https://github.com/Yuichi1001/2024-AICS-EXP
+
+### Old Edition Experiments
+
+- Old edition coursework: 6 experiments (including writing convolution operators, adding operators to TensorFlow, writing operators with BCL and integrating them into TensorFlow, etc.) (details can be found on the official website)
+- Old edition lab manuals: [Experiment 2.0 Instruction Manual](https://forum.cambricon.com/index.php?m=content&c=index&a=show&catid=155&id=708)
+- Learning notes: https://sanzo.top/categories/AI-Computing-Systems/, notes summarized from the lab manuals (link is no longer active)
+- @ysj1173886760 has compiled all resources and homework implementations used in this course at [ysj1173886760/Learning: ai-system - GitHub](https://github.com/ysj1173886760/Learning/tree/master/ai-system).

docs/机器学习系统/AICS.md

@@ -25,10 +25,21 @@
 - 课程网站：[官网](https://novel.ict.ac.cn/aics/)
 - 课程视频：[bilibili](https://space.bilibili.com/494117284)
 - 课程教材：智能计算系统（陈云霁）
-- 课程作业：6 个实验(包括编写卷积算子，为 TensorFlow 添加算子，用 BCL 编写算子并集成到 TensorFlow 中等)(具体内容在官网可以找到)
-- 实验手册：[实验 2.0 指导手册](https://forum.cambricon.com/index.php?m=content&c=index&a=show&catid=155&id=708)
-- 学习笔记：<https://sanzo.top/categories/AI-Computing-Systems/>，参考实验手册总结的笔记
 
 ## 资源汇总
 
-@ysj1173886760 在学习这门课中用到的所有资源和作业实现都汇总在 [ysj1173886760/Learning: ai-system - GitHub](https://github.com/ysj1173886760/Learning/tree/master/ai-system) 中。
+### 2024年新版实验
+
+- 2024 年的智能计算系统实验内容对知识体系、实验题目及实验手册进行了大范围的调整，调整内容包括全面使用 PyTorch ，不再使用 TensorFlow 以及添加大模型相关实验等。
+
+- 由于新版实验题目及实验手册未在寒武纪论坛进行更新，因此提供以下存储仓库，用于存储新版智能计算系统的实验题目、实验手册以及个人的实验答案
+- 新版实验的资源跟随国科大 2024 年春季学期的课程进度进行更新，预计 2024 年 6 月更新完毕
+- @Yuichi 编写的 2024 新版实验题目、手册及答案：https://github.com/Yuichi1001/2024-AICS-EXP
+
+### 旧版实验
+
+- 旧版课程作业：6 个实验(包括编写卷积算子，为 TensorFlow 添加算子，用 BCL 编写算子并集成到 TensorFlow 中等)(具体内容在官网可以找到)
+- 旧版实验手册：[实验 2.0 指导手册](https://forum.cambricon.com/index.php?m=content&c=index&a=show&catid=155&id=708)
+- 学习笔记：<https://sanzo.top/categories/AI-Computing-Systems/>，参考实验手册总结的笔记(已失效)
+- @ysj1173886760 在学习这门课中用到的所有资源和作业实现都汇总在 [ysj1173886760/Learning: ai-system - GitHub](https://github.com/ysj1173886760/Learning/tree/master/ai-system) 中。
+

docs/机器学习系统/CMU10-414.en.md

@@ -0,0 +1,30 @@
+# CMU 10-414/714: Deep Learning Systems
+
+## Course Overview
+
+- University: Carnegie Mellon University (CMU)
+- Prerequisites: Introduction to Systems (e.g., 15-213), Basics of Deep Learning, 
+                 Fundamental Mathematical Knowledge
+- Programming Languages: Python, C++
+- Difficulty: 🌟🌟🌟
+- Estimated Hours: 100 hours
+
+The rise of deep learning owes much to user-friendly frameworks like PyTorch and TensorFlow. Yet, many users remain unfamiliar with these frameworks' internals. If you're curious or aspiring to delve into deep learning framework development, this course is an excellent starting point.
+
+Covering the full spectrum of deep learning systems, the curriculum spans top-level framework design, autodifferentiation principles, hardware acceleration, and real-world deployment. The hands-on experience includes five assignments, building a deep learning library called Needle. Needle supports automatic differentiation, GPU acceleration, and various neural networks like CNNs, RNNs, LSTMs, and Transformers.
+
+Even for beginners, the course gradually covers simple classification and backpropagation optimization. Detailed Jupyter notebooks accompany complex neural networks, providing insights. For those with foundational knowledge, assignments post autodifferentiation are approachable, offering new understandings.
+
+Instructors [Zico Kolter](https://zicokolter.com/) and [Tianqi Chen](https://tqchen.com/)  released open-source content. Online evaluations and forums are closed, but local testing in framework code remains. Hope for an online version next fall.
+
+## Course Resources
+
+- Course Website：<https://dlsyscourse.org>
+- Course Videos：<https://www.youtube.com/watch?v=qbJqOFMyIwg>
+- Course Assignments：<https://dlsyscourse.org/assignments/>
+
+## Resource Compilation
+
+All resources and assignment implementations used by @PKUFlyingPig in this course are consolidated in [PKUFlyingPig/CMU10-714 - GitHub](https://github.com/PKUFlyingPig/CMU10-714)
+
+All assignment implementations by @Crazy-Ryan in this course (24 Fall offering) are consolidated in [Crazy-Ryan/CMU-10-714 - GitHub](https://github.com/Crazy-Ryan/CMU-10-714)

docs/机器学习系统/CMU10-414.md

@@ -32,3 +32,5 @@
 ## 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/CMU10-714 - GitHub](https://github.com/PKUFlyingPig/CMU10-714) 中。
+
+@Crazy-Ryan 在学习这门课(24 Fall)过程中的作业实现汇总在 [Crazy-Ryan/CMU-10-714 - GitHub](https://github.com/Crazy-Ryan/CMU-10-714) 中。

docs/机器学习系统/EML.en.md

@@ -0,0 +1,27 @@
+# MIT6.5940: TinyML and Efficient Deep Learning Computing
+
+## Descriptions
+
+- Offered by: MIT
+- Prerequisites: Computer architecture, Deep Learning
+- Programming Languages: Python
+- Difficulty: 🌟🌟🌟🌟
+- Class Hour: 50h
+
+This course, taught by MIT Professor [Song Han](https://hanlab.mit.edu/songhan), focuses on efficient machine learning techniques. Students are expected to have a pre-requisite of deep learning basics.
+
+The course is divided into three main sections. The first section covers various key techniques for lightweight neural networks, such as pruning, quantization, distillation, and neural architecture search (NAS). Building on these foundations, the second section introduces efficient optimization techniques tailored to specific application scenarios. These include cutting-edge topics in deep learning, such as inference for large language models, long-context support, post-training acceleration, multimodal large language models, GANs, diffusion models, and so on. The third section focuses on efficient training techniques, such as large-scale distributed parallelism, automatic parallel optimization, gradient compression, and on-device training. Professor Song Han’s lectures are clear and insightful, covering a wide range of topics, with a strong focus on trending areas. Those interested in gaining a foundational understanding of large language models may particularly benefit from the second and third sections.
+
+The course materials and resources are available on the course website. Official lecture videos can be found on YouTube, and both raw and subtitled versions are available on Bilibili. There are five assignments in total: the first three focus on quantization, pruning, and NAS, while the last two involve compression and efficient deployment of large language models. The overall difficulty is relatively manageable, making the assignments an excellent way to solidify core knowledge.
+
+
+## Course Resources
+
+- Course Website: [2024fall](https://hanlab.mit.edu/courses/2024-fall-65940), [2023fall](https://hanlab.mit.edu/courses/2023-fall-65940)
+- Recordings: [2024fall](https://www.youtube.com/playlist?list=PL80kAHvQbh-qGtNc54A6KW4i4bkTPjiRF), [2023fall](https://www.youtube.com/playlist?list=PL80kAHvQbh-pT4lCkDT53zT8DKmhE0idB)
+- Textbooks: None
+- Assignments: Five labs in total
+
+## Personal Resources
+
+All the resources and assignments used by @PKUFlyingPig in this course are maintained in [PKUFlyingPig/MIT6.5940_TinyML - GitHub](https://github.com/PKUFlyingPig/MIT6.5940_TinyML).

docs/机器学习系统/EML.md

@@ -0,0 +1,26 @@
+# MIT6.5940: TinyML and Efficient Deep Learning Computing
+
+## 课程简介
+
+- 所属大学：MIT
+- 先修要求：体系结构、深度学习基础、
+- 编程语言：Python
+- 课程难度：🌟🌟🌟🌟
+- 预计学时：50小时
+
+这门课由 MIT 的 [Song Han](https://hanlab.mit.edu/songhan) 教授讲授，侧重于高效的机器学习训练、推理技术。学生需要有一定的深度学习方面的知识基础。
+
+课程主要分为三个部分，首先讲授了让神经网络轻量化的各种关键技术，例如剪枝、量化、蒸馏、网络架构搜索等等。有了这些基础之后，课程第二部分会讲授面向特定领域场景的各种高效优化技术，涉及了目前深度学习最前沿热门的各个方向，例如大语言模型的推理、长上下文支持、后训练加速、多模态大语言模型、GAN、扩散模型等等。课程第三部分主要涉及各类高效训练技术，例如大规模分布式并行、自动并行优化、梯度压缩、边缘训练等等。Song Han 教授的讲解深入浅出，覆盖的知识面很广，且都是当前热门的领域方向，如果是想对大语言模型有初步了解也可以重点关注第二和第三部分的内容。
+
+课程内容和资源都可以在课程网站上找到，视频在油管上有官方版本，B站也有生肉和熟肉搬运，可以自行查找。课程作业一共有5个，前三个分别考察了量化、剪枝和 NAS，后两个主要是对大语言模型的压缩和高效部署，总体难度相对简单，但能很好地巩固核心知识。
+
+## 课程资源
+
+- 课程网站：[2024fall](https://hanlab.mit.edu/courses/2024-fall-65940), [2023fall](https://hanlab.mit.edu/courses/2023-fall-65940)
+- 课程视频：[2024fall](https://www.youtube.com/playlist?list=PL80kAHvQbh-qGtNc54A6KW4i4bkTPjiRF), [2023fall](https://www.youtube.com/playlist?list=PL80kAHvQbh-pT4lCkDT53zT8DKmhE0idB)
+- 课程教材：无
+- 课程作业：共5个实验，具体要求见课程网站
+
+## 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/MIT6.5940_TinyML - GitHub](https://github.com/PKUFlyingPig/MIT6.5940_TinyML) 中。

docs/机器学习系统/MLC.en.md

@@ -0,0 +1,24 @@
+# Machine Learning Compilation
+
+## Course Overview
+
+- University: Online course
+- Prerequisites: Foundations in Machine Learning/Deep Learning
+- Programming Language: Python
+--Difficulty: 🌟🌟🌟
+- Estimated Hours: 30 hours
+
+This course, offered by top scholar Chen Tianqi during the summer of 2022, focuses on the field of machine learning compilation. As of now, this area remains cutting-edge and rapidly evolving, with no dedicated courses available domestically or internationally. If you're interested in gaining a comprehensive overview of machine learning compilation, this course is worth exploring.
+
+The curriculum predominantly centers around the popular machine learning compilation framework [Apache TVM](https://tvm.apache.org/), co-founded by Chen Tianqi. It delves into transforming various machine learning models developed in frameworks like Tensorflow, Pytorch, and Jax into deployment patterns with higher performance and adaptability across different hardware. The course imparts knowledge at a relatively high level, presenting macro-level concepts. Each session is accompanied by a Jupyter Notebook that provides code-based explanations of the concepts. If you are involved in TVM-related programming and development, this course offers rich and standardized code examples for reference.
+
+All course resources are open-source, with versions available in both Chinese and English. The course recordings can be found on both Bilibili and YouTube in both languages.
+
+## Course Resources
+
+- Course Website：<https://mlc.ai/summer22-zh/>
+- Course Videos：[Bilibili][Bilibili_link]
+- Course Notes：<https://mlc.ai/zh/index.html>
+- Course Assignments：<https://github.com/mlc-ai/notebooks/blob/main/assignment>
+
+[Bilibili_link]: https://www.bilibili.com/video/BV15v4y1g7EU?spm_id_from=333.337.search-card.all.click&vd_source=a4d76d1247665a7e7bec15d15fd12349

docs/机器学习进阶/CMU10-708.en.md

@@ -0,0 +1,13 @@
+# CMU 10-708: Probabilistic Graphical Models
+
+## Course Introduction
+
+- **University**: Carnegie Mellon University (CMU)
+- **Prerequisites**: Machine Learning, Deep Learning, Reinforcement Learning
+- **Course Difficulty**: 🌟🌟🌟🌟🌟
+- **Course Website**: [CMU 10-708](https://sailinglab.github.io/pgm-spring-2019/)
+- **Course Resources**: The course website includes slides, notes, videos, homework, and project materials.
+
+CMU's course on Probabilistic Graphical Models, taught by Eric P. Xing, is a foundational and advanced course on graphical models. The curriculum covers the basics of graphical models, their integration with neural networks, applications in reinforcement learning, and non-parametric methods, making it a highly rigorous and comprehensive course.
+
+For students with a solid background in machine learning, deep learning, and reinforcement learning, this course provides a deep dive into the theoretical and practical aspects of probabilistic graphical models. The extensive resources available on the course website make it an invaluable learning tool for anyone looking to master this complex and rapidly evolving field.

docs/机器学习进阶/CMU10-708.md

@@ -6,6 +6,6 @@
 - 先修要求：Machine Learning, Deep Learning, Reinforcement Learning
 - 课程难度：🌟🌟🌟🌟🌟
 - 课程网站：<https://sailinglab.github.io/pgm-spring-2019/>
-  - 这个网站包含了所有的资源：slides, nots, video, homework, project
+- 课程网站包含了所有的资源：slides, notes, video, homework, and project
 
-这门课程是 CMU 的图模型基础 + 进阶课，授课老师为 Eric P. Xing，涵盖了图模型基础，与神经网络的结合，在强化学习中的应用，以及非参数方法。相当硬核
+这门课程是 CMU 的图模型基础 + 进阶课，授课老师为 Eric P. Xing，涵盖了图模型基础，与神经网络的结合，在强化学习中的应用，以及非参数方法，相当硬核。

docs/机器学习进阶/CS229M.en.md

@@ -0,0 +1,12 @@
+# STATS214 / CS229M: Machine Learning Theory
+
+## Course Introduction
+
+- **University**: Stanford
+- **Prerequisites**: Machine Learning, Deep Learning, Statistics
+- **Course Difficulty**: 🌟🌟🌟🌟🌟🌟
+- **Course Website**: [STATS214 / CS229M](http://web.stanford.edu/class/stats214/)
+
+This course offers a rigorous blend of classical learning theory and the latest developments in deep learning theory, making it exceptionally challenging and comprehensive. Previously taught by Percy Liang, the course is now led by Tengyu Ma, ensuring a high level of expertise and insight into the theoretical aspects of machine learning. 
+
+The curriculum is designed for students with a solid foundation in machine learning, deep learning, and statistics, aiming to deepen their understanding of the underlying theoretical principles in these fields. This course is an excellent choice for anyone looking to gain a thorough understanding of both the traditional and contemporary theoretical approaches in machine learning.

docs/机器学习进阶/CS229M.md

@@ -7,4 +7,4 @@
 - 课程难度：🌟🌟🌟🌟🌟🌟
 - 课程网站：<http://web.stanford.edu/class/stats214/>
 
-经典学习理论 + 最新深度学习理论，非常硬核。授课老师之前是 Percy Liang，现在是 Tengyu Ma
+经典学习理论 + 最新深度学习理论，非常硬核。授课老师之前是 Percy Liang，现在是 Tengyu Ma。

docs/机器学习进阶/STA4273.en.md

@@ -0,0 +1,14 @@
+# STA 4273 Winter 2021: Minimizing Expectations
+
+## Course Introduction
+
+- **University**: University of Toronto
+- **Prerequisites**: Bayesian Inference, Reinforcement Learning
+- **Course Difficulty**: 🌟🌟🌟🌟🌟🌟🌟
+- **Course Website**: [STA 4273 Winter 2021](https://www.cs.toronto.edu/~cmaddis/courses/sta4273_w21/)
+
+"Minimizing Expectations" is an advanced Ph.D. level research course, focusing on the interplay between inference and control. The course is taught by Chris Maddison, a founding member of AlphaGo and a NeurIPS 2014 best paper awardee.
+
+This course is notably challenging and is designed for students who have a strong background in Bayesian Inference and Reinforcement Learning. The curriculum explores deep theoretical concepts and their practical applications in the fields of machine learning and artificial intelligence.
+
+Chris Maddison's expertise and his significant contributions to the field, particularly in the development of AlphaGo, make this course highly prestigious and insightful for Ph.D. students and researchers looking to deepen their understanding of inference and control in advanced machine learning contexts. The course website provides valuable resources for anyone interested in this specialized area of study.

docs/机器学习进阶/STA4273.md

@@ -7,4 +7,4 @@
 - 课程难度：🌟🌟🌟🌟🌟🌟🌟
 - 课程网站：<https://www.cs.toronto.edu/~cmaddis/courses/sta4273_w21/>
 
-这是一门较为进阶的 Ph.D. 研究课程，核心内容是 inference 和 control 之间的关系。授课老师为 Chris Maddison (AlphaGo founding member, NeurIPS 14 best paper)
+这是一门较为进阶的 Ph.D. 研究课程，核心内容是 inference 和 control 之间的关系。授课老师为 Chris Maddison (AlphaGo founding member, NeurIPS 14 best paper)。

docs/机器学习进阶/STAT8201.en.md

@@ -0,0 +1,14 @@
+# Columbia STAT 8201: Deep Generative Models
+
+## Course Introduction
+
+- **University**: Columbia University
+- **Prerequisites**: Machine Learning, Deep Learning, Graphical Models
+- **Course Difficulty**: 🌟🌟🌟🌟🌟🌟
+- **Course Website**: [STAT 8201](http://stat.columbia.edu/~cunningham/teaching/GR8201/)
+
+"Deep Generative Models" is a Ph.D. level seminar course at Columbia University, taught by John Cunningham. This course is structured around weekly paper presentations and discussions, focusing on deep generative models, which represent the intersection of graphical models and neural networks and are one of the most important directions in modern machine learning.
+
+The course is designed to explore the latest advancements and theoretical foundations in deep generative models. Participants engage in in-depth discussions about current research papers, fostering a deep understanding of the subject matter. This format not only helps students keep abreast of the latest developments in this rapidly evolving field but also sharpens their critical thinking and research skills.
+
+Given the advanced nature of the course, it is ideal for Ph.D. students and researchers who have a solid foundation in machine learning, deep learning, and graphical models, and are looking to delve into the cutting-edge of deep generative models. The course website provides a valuable resource for accessing the curriculum and related materials.

docs/机器学习进阶/STAT8201.md

@@ -7,4 +7,4 @@
 - 课程难度：🌟🌟🌟🌟🌟🌟
 - 课程网站：<http://stat.columbia.edu/~cunningham/teaching/GR8201/>
 
-这门课是一门 PhD 讨论班，每周的内容是展示 + 讨论论文，授课老师是 John Cunningham。Deep Generative Models （深度生成模型） 是图模型与神经网络的结合，也是现代机器学习最重要的方向之一
+这门课是一门 PhD 讨论班，每周的内容是展示 + 讨论论文，授课老师是 John Cunningham。Deep Generative Models （深度生成模型） 是图模型与神经网络的结合，也是现代机器学习最重要的方向之一。

docs/机器学习进阶/roadmap.en.md

@@ -0,0 +1,56 @@
+# Advanced Machine Learning Roadmap
+
+This learning path is suitable for students who have already learned the basics of machine learning (ML, NLP, CV, RL), such as senior undergraduates or junior graduate students, and have published at least one paper in top conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, CVPR, ICCV) and are interested in pursuing a research path in machine learning.
+
+The goal of this path is to lay the theoretical groundwork for understanding and publishing papers at top machine learning conferences, especially in the track of Probabilistic Methods.
+
+There can be multiple advanced learning paths in machine learning, and this one represents the best path as understood by the author [Yao Fu](https://franxyao.github.io/), focusing on probabilistic modeling methods under the Bayesian school and involving interdisciplinary knowledge.
+
+## Essential Textbooks
+
+- PRML: Pattern Recognition and Machine Learning by Christopher Bishop
+- AoS: All of Statistics by Larry Wasserman
+
+These two books respectively represent classic teachings of the Bayesian and frequentist schools, complementing each other nicely.
+
+## Reference Books
+
+- MLAPP: Machine Learning: A Probabilistic Perspective by Kevin Murphy
+- Convex Optimization by Stephen Boyd and Lieven Vandenberghe
+
+## Advanced Books
+
+- W&J: Graphical Models, Exponential Families, and Variational Inference by Martin Wainwright and Michael Jordan
+- Theory of Point Estimation by E. L. Lehmann and George Casella
+
+## Reading Guidelines
+
+### How to Approach
+
+- Essential textbooks are a must-read.
+- Reference books are like dictionaries: consult them when encountering unfamiliar concepts (instead of Wikipedia).
+- Advanced books should be approached after completing the essential textbooks, which should be read multiple times for thorough understanding.
+- Contrastive-comparative reading is crucial: open two books on the same topic, compare similarities, differences, and connections.
+- Recall previously read papers during reading and compare them with textbook content.
+
+### Basic Pathway
+
+1. Start with AoS Chapter 6: Models, Statistical Inference, and Learning as a basic introduction.
+2. Read PRML Chapters 10 and 11:
+   - Chapter 10 covers Variational Inference, and Chapter 11 covers MCMC, the two main routes for Bayesian inference.
+   - Consult earlier chapters in PRML or MLAPP for any unclear terms.
+   - AoS Chapter 8 (Parametric Inference) and Chapter 11 (Bayesian Inference) can also serve as references. Compare these chapters with the relevant PRML chapters.
+3. After PRML Chapters 10 and 11, proceed to AoS Chapter 24 (Simulation Methods) and compare it with PRML Chapter 11, focusing on MCMC.
+4. If foundational concepts are still unclear, review PRML Chapter 3 and compare it with AoS Chapter 11.
+5. Read PRML Chapter 13 (skip Chapter 12) and compare it with MLAPP Chapters 17 and 18, focusing on HMM and LDS.
+6. After completing PRML Chapter 13, move on to Chapter 8 (Graphical Models).
+7. Cross-reference these topics with CMU 10-708 PGM course materials.
+
+By this point, you should have a grasp of:
+
+- Basic definitions of probabilistic models
+- Exact inference - Sum-Product
+- Approximate inference - MCMC
+- Approximate inference - VI
+
+Afterward, you can proceed to more advanced topics.

docs/机器学习进阶/roadmap.md

@@ -1,19 +1,17 @@
-# 机器学习进阶
+# 机器学习进阶学习路线
 
 此路线图适用于已经学过了基础机器学习 (ML, NLP, CV, RL) 的同学 (高年级本科生或低年级研究生)，已经发表过至少一篇顶会论文 (NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, CVPR, ICCV) 想要走机器学习科研路线的选手。
 
-此路线的目标是为读懂与发表机器学习顶会论文打下理论基础，特别是 Probabilistic Methods 这个 track 下的文章
+此路线的目标是为读懂与发表机器学习顶会论文打下理论基础，特别是 Probabilistic Methods 这个 track 下的文章。
 
 机器学习进阶可能存在多种不同的学习路线，此路线只能代表作者 [Yao Fu](https://franxyao.github.io/) 所理解的最佳路径，侧重于贝叶斯学派下的概率建模方法，也会涉及到各项相关学科的交叉知识。
 
 ## 必读教材
 
 - PRML: Pattern Recognition and Machine Learning. Christopher Bishop
-  - 经典贝叶斯学派教材
 - AoS: All of Statistics. Larry Wasserman
-  - 经典频率学派教材
 
-所以这两本书刚好相辅相成
+这两本书分别是经典贝叶斯学派和经典频率学派的教材，刚好相辅相成。
 
 ## 字典
 
@@ -55,11 +53,11 @@
 - 读完 PRML 第 13 章之后，再去读 PRML 第 8 章 (Graphical Models) -- 此时这部分应该会读得很轻松
 - 以上的内容可以进一步对照 CMU 10-708 PGM 课程材料
 
-到目前为止，应该能够掌握
+到目前为止，应该能够掌握:
 
 - 概率模型的基础定义
 - 精准推断 - Sum-Product
 - 近似推断 - MCMC
 - 近似推断 - VI
 
-然后就可以去做更进阶的内容
+然后就可以去做更进阶的内容。

docs/深度学习/CS224n.en.md

@@ -0,0 +1,27 @@
+# CS224n: Natural Language Processing
+
+## Course Overview
+
+- University：Stanford
+- Prerequisites：Fundations of Deep Learning + Python
+- Programming Language：Python
+- Course Difficulty：🌟🌟🌟🌟
+- Estimated Hours：80 hours
+
+CS224n is an introductory course in Natural Language Processing (NLP) offered by Stanford and led by renowned NLP expert Chris Manning. The course covers core concepts in the field of NLP, including word embeddings, RNNs, LSTMs, Seq2Seq models, machine translation, attention mechanisms, Transformers, and more.
+
+The course consists of 5 progressively challenging programming assignments covering word vectors, the word2vec algorithm, dependency parsing, machine translation, and fine-tuning a Transformer.
+
+The final project involves training a Question Answering (QA) model on the well-known SQuAD dataset. Some students' final projects have even led to publications in top conferences.
+
+## Course Resources
+
+- Course Website：<http://web.stanford.edu/class/cs224n/index.html>
+- Course Videos: Search for 'CS224n' on Bilibili <https://www.bilibili.com/>
+- Course Textbook：N/A
+- Course Assignments：<http://web.stanford.edu/class/cs224n/index.html>，5 Programming Assignments + 1 Final Project
+
+## Resource Compilation
+
+All resources and assignment implementations used by @PKUFlyingPig during the course are compiled in [PKUFlyingPig/CS224n - GitHub](https://github.com/PKUFlyingPig/CS224n)
+

docs/深度学习/CS224n.md

@@ -8,7 +8,7 @@
 - 课程难度：🌟🌟🌟🌟
 - 预计学时：80 小时
 
-Stanford 的 NLP 入门课程，由自然语言处理领域的巨佬 Chris Manning 领衔教授（word2vec 算法的开创者）。内容覆盖了词向量、RNN、LSTM、Seq2Seq 模型、机器翻译、注意力机制、Transformer 等等 NLP 领域的核心知识点。
+Stanford 的 NLP 入门课程，由自然语言处理领域的巨佬 Chris Manning 领衔教授。内容覆盖了词向量、RNN、LSTM、Seq2Seq 模型、机器翻译、注意力机制、Transformer 等等 NLP 领域的核心知识点。
 
 5 个编程作业难度循序渐进，分别是词向量、word2vec 算法、Dependency parsing、机器翻译以及 Transformer 的 fine-tune。
 

docs/深度学习/CS224w.en.md

@@ -0,0 +1,18 @@
+# CS224w: Machine Learning with Graphs
+
+## Descriptions
+
+- Offered by: Stanford
+- Prerequisites: fundamental machine learning + Python
+- Programming Language: Python, LaTeX
+- Difficulty: 🌟🌟🌟🌟
+- Class Hour: 80 hours
+
+Stanford's Introduction to Graph Neural Networks course, I haven't taken this course, but many friends who are focusing on GNN have recommended it to me, so I guess Stanford's course quality is still guaranteed as always. The instructor of this course is very young and handsome :)
+
+## Course Resources
+
+- Course Website: <http://web.stanford.edu/class/cs224w/>
+- Lecture Videos: <https://www.youtube.com/watch?v=JAB_plj2rbA>
+- Text Book: none
+- Assignments: <http://web.stanford.edu/class/cs224w/>,  with 6 programming assignments, 3 LaTeX written assignments

docs/深度学习/CS231.en.md

@@ -0,0 +1,18 @@
+# CS231n: CNN for Visual Recognition
+
+## Course Introduction
+
+- Affiliated Universities：Stanford
+- Prerequisites: Foundations of Machine Learning
+- Programming Languages：Python
+- Course Difficulty：🌟🌟🌟🌟
+- Estimated hours: 80 hours
+
+Stanford's CV introductory class, led by the giant of the computer field, Fei-Fei Li (the research team of the epoch-making famous dataset ImageNet in CV field), but its content is relatively basic and friendly, if you have taken CS230, you can directly start the Project as practice.
+
+## Course Resources
+
+- Course Website：<http://cs231n.stanford.edu/>
+- Course Video：<https://www.bilibili.com/video/BV1nJ411z7fe>
+- Course Materials: None
+- Coursework：<http://cs231n.stanford.edu/schedule.html>，3 Programming Assignments

docs/深度学习/CS285.en.md

@@ -0,0 +1,22 @@
+# CS285: Deep Reinforcement Learning
+
+## Course Overview
+
+- University：UC Berkeley
+- Prerequisites：CS188, CS189
+- Programming Language：Python
+- Course Difficulty：🌟🌟🌟🌟
+- Estimated Hours：80 hours
+
+The CS285 course, currently taught by Professor Sergey Levine, covers various aspects of deep reinforcement learning. It is suitable for students with a foundational understanding of machine learning, including concepts such as Markov Decision Processes (MDPs). The course involves a substantial amount of mathematical formulas, so a reasonable mathematical background is recommended. Additionally, the professor regularly updates the course content and assignments to reflect the latest research developments, making it a dynamic learning experience.
+
+For course content access, as of the Fall 2022 semester, the teaching format involves pre-recorded videos for students to watch before class. The live sessions mainly focus on Q&A, where the professor discusses selected topics from the videos and answers students' questions. Therefore, the provided course video links already include all the content. The assignments consist of five programming projects, each involving the implementation and comparison of classical models. Occasionally, assignments may also include the reproduction of recent models. The final submission typically includes a report. Given that assignments provide a framework and often involve code completion based on hints, the difficulty level is not excessively high.
+
+In summary, this course is suitable for beginners entering the field of deep reinforcement learning. Although the difficulty increases as the course progresses, it offers a rewarding learning experience.
+
+## Course Resources
+
+- Course Website: <http://rail.eecs.berkeley.edu/deeprlcourse/>
+- Course Videos: <https://www.youtube.com/playlist?list=PL_iWQOsE6TfX7MaC6C3HcdOf1g337dlC9>
+- Course Texbook: N/A
+- Course Assignments: <http://rail.eecs.berkeley.edu/deeprlcourse/>, 5 programming assignments

docs/深度学习/EECS498-007.en.md

@@ -0,0 +1,62 @@
+# UMich EECS 498-007 / 598-005: Deep Learning for Computer Vision
+
+## Course Introduction
+
+- Offered by: UMich
+- Prerequisites: Basic Python, Matrix Theory (familiarity with matrix derivation is sufficient), Calculus
+- Programming Languages: Python
+- Difficulty: 🌟🌟🌟🌟
+- Class Hour: 60 ~ 80 hours
+
+The University of Michigan's Computer Vision course is of exceptionally high quality, with its videos and assignments covering an extensive range of topics. 
+
+The assignments gradually increase in difficulty and cover all stages of mainstream CV model development, making this an excellent introductory course for Computer Vision.
+
+In each assignment, you'll build and train models or frameworks mentioned in the lectures, following the provided handouts.
+
+You don't need any prior experience with deep learning frameworks. 
+
+The course will teach you from scratch how to use Pytorch in the early assignments, and it can subsequently serve as a reference book for you.
+
+As each assignment deals with different themes, you'll not only gain a first-hand understanding of the development of mainstream CV models through these progressive assignments but also appreciate the impacts of different models and training methods on final performance and accuracy. 
+
+Moreover, you'll get hands-on experience in implementing them.
+
+In Assignment 1 (A1), you'll learn how to use Pytorch and Google Colab.
+
+In Assignment 2 (A2), you will build a Linear Classifier and a two-layer neural network. Finally, you'll have the opportunity to work with the MNIST dataset, on which you will train and evaluate your neural network.
+
+In Assignment 3 (A3), you'll encounter the classic Convolutional Neural Network (CNN) and experience the power of convolutional neural networks.
+
+In Assignment 4 (A4), you'll have the opportunity to build an object detection model from scratch, following the handout to implement a One-Stage Detector and a Two-Stage Detector from two research papers.
+
+By Assignment 5 (A5), you'll transition from CNN to RNN. You'll have the opportunity to build two different attention-based models, RNNs (Vanilla RNN & LSTM), and the famous Transformer.
+
+In the final assignment (A6), you'll get a chance to implement two more advanced models, VAE and GAN, and apply them to the MNIST dataset. Finally, you'll implement two very cool features: network visualization and style transfer.
+
+Beyond the assignments, you can also implement a Mini-Project, building a complete deep learning pipeline. You can refer to the course homepage for specifics.
+
+All the resources involved in the course, such as lectures, notes, and assignments, are open source. 
+
+The only downside is that the Autograder is only available to students enrolled at the University of Michigan. 
+
+However, given that the correctness of the implementation and the expected results can already be confirmed in the provided *.ipynb (i.e., the Handout), I personally feel that the absence of Autograder doesn't affect the learning process.
+
+It's worth mentioning that the main lecturer for this course, Justin Johnson, is a Ph.D. graduate of Fei-Fei Li and currently an Assistant Professor at the University of Michigan.
+
+The open-source 2017 version of Stanford's CS231N was taught by Justin Johnson.
+
+Because CS231N was mainly developed by Justin Johnson and Andrej Karpathy, this course also adopts some materials from CS231N. 
+
+Therefore, students who have studied CS231N might find some materials in this course familiar.
+
+Lastly, I recommend every student enrolled in this course to watch the lectures on YouTube. Justin Johnson's teaching style and content are very clear and easy to understand, making them a fantastic resource.
+
+## Course Resources
+
+- Course Website：<https://web.eecs.umich.edu/~justincj/teaching/eecs498/WI2022/>
+- Course Video：<https://www.youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r>
+- Course Materials: Only recommended textbooks, link: <https://www.deeplearningbook.org/>
+- Coursework：See the course homepage for details, six Assignments and one Mini-Project
+## Personal Resources
+@Michael-Jetson The 200,000 to 300,000 words of notes I have taken (and did not include homework, etc.) can be used as a reference:[Michael-Jetson/ML_DL_CV_with_pytorch](https://github.com/Michael-Jetson/ML_DL_CV_with_pytorch)

docs/深度学习/EECS498-007.md

@@ -0,0 +1,50 @@
+# UMich EECS 498-007 / 598-005: Deep Learning for Computer Vision
+
+## 课程简介
+
+- 所属大学：UMich
+- 先修要求：Python基础，矩阵论(熟悉矩阵求导即可)，微积分
+- 编程语言：Python
+- 课程难度：🌟🌟🌟🌟
+- 预计学时：60～80 小时
+
+UMich 的 Computer Vision 课，课程视频和作业质量极高，涵盖的主题非常全，同时 Assignments 的难度由浅及深，覆盖了 CV 主流模型发展的全阶段，是一门非常好的 Computer Vision 入门课。
+
+你在每个 Assignment 里会跟随 Handouts 搭建与训练 Lectures 中提到的模型/框架。
+
+你不需要有任何的深度学习框架的使用经验，在开始的 Assignment 里，这门课会从零开始教导每个学生如何使用 Pytorch，后续也可以当成工具书，随时翻阅。
+
+同时由于每个 Assignment 之间涉及到的主题都不同，你在递进式的 Assignment 中不仅可以亲身体会到 CV 主流模型的发展历程，领略到不同的模型和训练的方法对最终效果/准确率的影响，同时也能 Hands On 地实现它们。
+
+在 A1 中，你会学习 Pytorch 和 Google Colab 的使用。
+
+在 A2 中你会亲自搭建 Linear Classifier 以及一个两层的神经网络，最后你有机会亲自接触 MNIST 数据集并在此基础上训练并评估你搭建起的神经网络。
+
+在 A3 中，你会接触到最为经典的 Convolutional Neural Network (A.K.A. CNN)，亲自感受卷积神经网络的魅力。
+
+而在 A4 中，你将实际触及搭建物体检测模型的全流程，同时跟随 Handout 实现两篇论文中的 One-Stage Detector 和 Two-Stage Detector。
+
+到了 A5，就是从 CNN 到 RNN 的时刻了，你将有机会亲自搭建起两种不同的基于注意力的模型，RNNs (Vanilla RNN & LSTM) 和大名鼎鼎的 Transfomer。
+
+在最后一个 Assignment（A6）中，你将有机会实现两种更为 Fancy 的模型，VAE 和 GAN，并应用在 MINST 数据集上。最后，你会实现网络可视化和风格迁移这两个非常酷炫的功能。
+
+在 Assignments 之外，你还可以自己实现一个 Mini-Project，亲自搭建起一个完整的深度学习 Pipeline，具体可以参考课程主页。
+
+课程所涉及的资源，如 Lectures/Notes/Assignments 都是开源的，美中不足的是 Autograder 只对本校 Enrolled 的学生开放，但因为在提供的 `*.ipynb`（也就是 Handout） 中已经可以确定实现的正确性，以及预期的结果，所以我个人觉得 Autograder 的缺失没有任何影响。
+
+值得一提的是，这门课的主讲教授 Justin Johnson 正是 Fei-Fei Li 的博士毕业生，现在在 UMich 当 Assistant Professor。
+
+而现在开源的 2017 年版本的 Stanford CS231N 的主讲人就是 Justin Johnson。
+
+同时因为 CS231N 主要是由 Justin Johnson 和 Andrej Karpathy 建设起来的，这门课也沿用了 CS231N 的一些材料，所以学过 CS231N 的同学可能会觉得这门课的某些材料比较熟悉。
+
+最后，我推荐每一个 Enroll 这门课的同学都去看一看 Youtube 上面的 Lectures，Justin Johnson 的讲课方式和内容都非常清晰和易懂，是非常棒的参考。
+
+## 课程资源
+
+- 课程网站：<https://web.eecs.umich.edu/~justincj/teaching/eecs498/WI2022/>
+- 课程视频：<https://www.youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r>
+- 课程教材：仅有推荐教材，链接：<https://www.deeplearningbook.org/>
+- 课程作业：见课程主页，6 个 Assignment 和一个 Mini-Project
+## 资源汇总
+@Michael-Jetson 本人所做的二三十万字的笔记（并没有包括作业等），可以当做一个参考[Michael-Jetson/ML_DL_CV_with_pytorch](https://github.com/Michael-Jetson/ML_DL_CV_with_pytorch)

docs/深度学习/LHY.en.md

@@ -0,0 +1,24 @@
+# National Taiwan University: Machine Learning by Hung-yi Lee
+
+## Course Overview
+
+- University: National Taiwan University
+- Prerequisites: Proficiency in Python
+- Programming Language: Python
+- Course Difficulty: 🌟🌟🌟🌟
+- Estimated Hours：80 hours
+
+Professor Hung-yi Lee, a professor at National Taiwan University, is known for his humorous and engaging teaching style. He often incorporates fun elements like Pokémon into his slides, making the learning experience enjoyable.
+
+Although labeled as a machine learning course, the breadth of topics covered is impressive. The course includes a total of 15 labs covering Regression, Classification, CNN, Self-Attention, Transformer, GAN, BERT, Anomaly Detection, Explainable AI, Attack, Adaptation, RL, Compression, Life-Long Learning, and Meta Learning. This wide coverage allows students to gain insights into various domains of deep learning, helping them choose areas for further in-depth study.
+
+Don't be overly concerned about the difficulty of the assignments. All assignments come with example code from teaching assistants, guiding students through data processing, model building, and more. Students are required to make modifications based on the provided code. This presents an excellent opportunity to learn from high-quality code, and the assignments serve as valuable resources for those looking to breeze through course projects.
+
+The 2025 version of the course has undergone a reform of the course content, focusing more on RAG, AI Agent, LLM all sorts of fancier content; it differs greatly from the 2023 version and previous versions
+
+## Course Resources
+
+- Course Websites：[Spring2023](https://speech.ee.ntu.edu.tw/~hylee/ml/2023-spring.php), [Spring2025](https://speech.ee.ntu.edu.tw/~hylee/ml/2025-spring.php)
+- Course Videos：[Spring2023](https://speech.ee.ntu.edu.tw/~hylee/ml/2023-spring.php), [Spring2025](https://speech.ee.ntu.edu.tw/~hylee/ml/2025-spring.php)
+- Course Textbook: N/A
+- Course Assignments：[Spring2023](https://speech.ee.ntu.edu.tw/~hylee/ml/2023-spring.php)(15 labs covering a wide range of deep learning domains), [Spring2025](https://speech.ee.ntu.edu.tw/~hylee/ml/2025-spring.php) (focus on  LLM related work like AI Agent)

docs/深度学习/LHY.md

@@ -15,9 +15,11 @@ RL、Compression、Life-Long Learning 以及 Meta Learning。可谓是包罗万
 
 大家也大可不必担心作业的难度，因为所有作业都会提供助教的示例代码，帮你完成数据处理、模型搭建等，你只需要在其基础上进行适量的修改即可。这也是一个学习别人优质代码的极好机会，大家需要水课程大作业的话，这里也是一个不错的资料来源。
 
+2025年版课程的课程内容发生改革，更加侧重于RAG、AI Agent、LLM种种更fasion的内容；与2023版及之前版本差异极大
+
 ## 课程资源
 
-- 课程网站：<https://speech.ee.ntu.edu.tw/~hylee/ml/2022-spring.php>
-- 课程视频：<https://speech.ee.ntu.edu.tw/~hylee/ml/2022-spring.php>，每节课的链接参见课程网站
+- 课程网站：[Spring2023](https://speech.ee.ntu.edu.tw/~hylee/ml/2023-spring.php), [Spring2025](https://speech.ee.ntu.edu.tw/~hylee/ml/2025-spring.php)
+- 课程视频：[Spring2023](https://speech.ee.ntu.edu.tw/~hylee/ml/2023-spring.php), [Spring2025](https://speech.ee.ntu.edu.tw/~hylee/ml/2025-spring.php)，每节课的链接参见课程网站
 - 课程教材：无
-- 课程作业：<https://speech.ee.ntu.edu.tw/~hylee/ml/2022-spring.php>，15 个 lab，几乎覆盖了主流深度学习的所有领域
+- 课程作业：[Spring2023](https://speech.ee.ntu.edu.tw/~hylee/ml/2023-spring.php) (5 个 lab，几乎覆盖了主流深度学习的所有领域；部分作业colab上可能无法打开，这时候可以参考弘毅老师的github), [Spring2025](https://speech.ee.ntu.edu.tw/~hylee/ml/2025-spring.php) (主要关注 AI Agent 等 LLM 相关领域)

docs/深度生成模型/MIT6.S184.en.md

@@ -0,0 +1,20 @@
+# MIT6.S184: Generative AI with Stochastic Differential Equations
+
+## Course Introduction
+
+- University: MIT  
+- Prerequisites: Basic understanding of deep learning, and be comfortable with calculus and linear algebra  
+- Programming Language: Python (with PyTorch)  
+- Course Difficulty: 🌟🌟🌟🌟  
+- Estimated Study Hours: 20  
+
+This course is an introductory diffusion model course offered during MIT's IAP term by MIT CSAIL. Taught by MIT students Peter Holderrieth and Ezra Erives, the course provides a clear and accessible explanation of the mathematical foundations of diffusion and flow-matching models from the perspective of differential equations. It also includes hands-on labs where students build diffusion models from scratch, concluding with lectures on applications in cutting-edge areas such as molecular design and robotics.
+
+The accompanying lecture notes are exceptionally well-written and highly recommended for in-depth reading.
+
+## Course Resources
+
+- Course Website: https://diffusion.csail.mit.edu/  
+- Course Videos: See course website  
+- Course Textbook: [An Introduction to Flow Matching and Diffusion Models](https://arxiv.org/abs/2506.02070)  
+- Course Assignments: Three labs, see course website for details  

docs/深度生成模型/MIT6.S184.md

@@ -0,0 +1,20 @@
+# MIT6.S184: Generative AI with Stochastic Differential Equations
+
+## 课程简介
+
+- 所属大学：MIT
+- 先修要求：Basic understanding of deep learning, and be comfortable with calculus and linear algebra
+- 编程语言：Python (with PyTorch)
+- 课程难度：🌟🌟🌟🌟
+- 预计学时：20
+
+这门课程是由 MIT CSAIL 的 IAP 小学期开办的扩散模型入门课程。该课程由 MIT 学生 Peter Holderrieth 和 Ezra Erives 主讲，从微分方程的视角深入浅出地讲解了扩散模型和流匹配模型的数学理论基础，并且配以实践让学生从零构建扩散模型，最后通过讲座介绍其在分子设计和机器人学等前沿技术中的应用。
+
+课程配套的教材笔记写得非常好，推荐仔细阅读。
+
+## 课程资源
+
+- 课程网站：https://diffusion.csail.mit.edu/
+- 课程视频：参见课程网站
+- 课程教材：[An Introduction to Flow Matching and Diffusion Models](https://arxiv.org/abs/2506.02070)
+- 课程作业：三个实验，具体参见课程网站

docs/深度生成模型/roadmap.en.md

@@ -0,0 +1,29 @@
+# Deep Generative Models Roadmap
+
+In recent years, large language models (LLMs) have become a hot topic, which is also highly relevant to the author’s PhD research. This roadmap aims to share various course materials and references that the author found useful while getting familiar with and delving into the field of deep generative models. It’s intended to help practitioners in related areas or anyone interested in the underlying principles of generative models. Due to limited time outside of research, the author has not completed all the course assignments; updates will be added gradually when time permits.
+
+In fact, LLMs are just one branch of deep generative models. Other types such as VAEs, GANs, Diffusion Models, and Flows still play an important role in the broader domain of "generation." The term AIGC (AI-generated content) broadly refers to these technologies.
+
+Recommended courses for learning:
+
+- [MIT 6.S184: Generative AI with Stochastic Differential Equations](./MIT6.S184.md): An introductory GenAI course offered during MIT's IAP term. It explains the mathematical foundations behind Flow Matching and Diffusion Models from the perspective of differential equations, accompanied by simple hands-on labs to help students grasp the concepts through practice. Ideal for those interested in the underlying mathematical principles.
+
+- [MIT 6.S978: Deep Generative Models](https://mit-6s978.github.io/schedule.html): Taught by MIT’s rising star Prof. Kaiming He, this course covers fundamental theories and cutting-edge papers related to various generative models. The assignments include well-prepared scaffold code. While not overly difficult, they help deepen understanding and provide a quick, comprehensive view of the field.
+
+- [UCB CS294-158-SP24: Deep Unsupervised Learning](https://sites.google.com/view/berkeley-cs294-158-sp24/home): Taught by reinforcement learning giant Pieter Abbeel. Compared to the MIT course, this one is more comprehensive and includes lecture videos and slides. The homework only provides test code, so students must implement model architecture and training code themselves. Though demanding, it’s ideal for those who want hands-on experience in training models. As is well known, there are many practical tricks in deep learning, and the devil is often in the details. Nothing teaches those details better than training a model yourself.
+
+- [CMU 10423: Generative AI](https://www.cs.cmu.edu/~mgormley/courses/10423/schedule.html): CMU’s GenAI course focuses more on large language models compared to the previous two, but shares much of the same content otherwise. The assignments are quite engaging and worth trying out in your spare time.
+
+The GPT series by OpenAI has demonstrated remarkable performance under the guidance of scaling laws, especially in mathematics and coding. If you are primarily interested in LLMs, the following courses are recommended:
+
+- [Stanford CS336: Language Modeling from Scratch](https://stanford-cs336.github.io/spring2025/index.html): As the title suggests, this course teaches you to build all the core components of an LLM from scratch, such as the tokenizer, model architecture, training optimizer, low-level operators, data cleaning, and post-training algorithms. Each assignment has a 40-50 page PDF handout—very rigorous. Highly recommended if you want to fully understand every low-level detail of LLMs.
+
+- [CMU 11868: Large Language Model Systems](https://llmsystem.github.io/llmsystem2025spring/): This CMU course focuses on system-level optimization of LLMs, including GPU acceleration, distributed training/inference, and cutting-edge techniques. Great for students in systems research to gain a holistic understanding of the field. (Disclosure: One of my papers on PD decoupling is included in the syllabus, hence the personal recommendation.) Assignments involve implementing a mini-PyTorch framework and then building system-level LLM optimizations on top of it.
+
+- [CMU 11667: Large Language Models: Methods and Applications](https://cmu-llms.org/) and [CMU 11711: Advanced NLP](https://www.phontron.com/class/anlp-fall2024/): Compared to the previous two, these courses focus more on higher-level algorithms and applications. Each lecture includes many recommended readings, making them suitable for gaining a broad understanding of LLM research frontiers. You can then dive deeper into any subfield that interests you based on the reading materials.
+
+In addition to courses, the following resources are also highly recommended:
+
+- [Awesome-LLM](https://github.com/Hannibal046/Awesome-LLM): A curated list of LLM-related resources.
+- [LLMSys-PaperList](https://github.com/AmberLJC/LLMSys-PaperList): A collection of system-related papers on LLMs.
+- [MLsys-Guide](https://github.com/PKU-DAIR/Starter-Guide/blob/main/docs/systems/Readme.md): A beginner’s guide to deep learning systems.

docs/深度生成模型/roadmap.md

@@ -0,0 +1,29 @@
+# 深度生成模型学习路线
+
+近几年大语言模型成为大热的方向，也和笔者博士期间的课题非常相关。这篇路线图旨在分享笔者在熟悉和深入深度生成模型这一领域过程中学习和参考的各类课程资料，方便相关领域的从业者或者对生成模型的底层原理感兴趣的朋友共同学习。由于笔者科研之余时间有限，很多课程的实验并未完成，等后续有时间完成之后会在该目录下一一添加。
+
+其实，大语言模型只是深度生成模型的一个分支，而其他生成模型例如 VAE，GAN，Diffusion Model，Flow 等等，都还在“生成”这一领域占有重要地位，所谓的 AIGC，就是泛指这一类技术。
+推荐学习下列课程：
+
+- [MIT 6.S184: Generative AI with Stochastic Differential Equations](./MIT6.S184.md): MIT IAP 小学期的 GenAI 入门课程，主要通过微分方程的视角讲解了 Flow Matching 和 Diffusion Model 背后的数学原理，并且配有简单的小实验让学生在实践中理解，适合对底层数学原理感兴趣的同学入门。
+
+- [MIT 6.S978: Deep Generative Models](https://mit-6s978.github.io/schedule.html): MIT 新晋明星教授何恺明亲授，涵盖了各种生成模型的基础理论和相关前沿论文，几次作业都有丰富的脚手架代码，难度不高但能加深理解，能对这个领域有个快速全貌了解。
+
+- [UCB CS294-158-SP24: Deep Unsupervised Learning](https://sites.google.com/view/berkeley-cs294-158-sp24/home): 强化学习领域的顶级巨佬 Pieter Abbeel 主讲，相比 MIT 的课程内容更加丰富全面，并且有配套课程视频和 Slides。此外课后作业只有测试代码，需要学生自主编写模型架构定义和训练代码，虽然硬核但很适合有志于炼丹的同学练手。众所周知，深度学习理论实践中存在着很多经验技巧，魔鬼往往存在于细节里。没有什么比自己上手训一个模型更能掌握这些细节了。
+
+- [CMU 10423: Generative AI](https://www.cs.cmu.edu/~mgormley/courses/10423/schedule.html): CMU 的 GenAI 课程，相比前两门课更侧重于大语言模型一些，其他内容和前两门课重合较多。不过课程作业都挺有意思，推荐闲暇时间练练手。
+
+
+OpenAI 的 GPT 系列让大语言模型在 Scaling Law 的指引下展现出惊人的效果，在数学和代码领域取得了很大进展。如果你主要关注大语言模型这个方向，那么推荐如下课程：
+
+- [Stanford CS336: Language Modeling from Scratch](https://stanford-cs336.github.io/spring2025/index.html): 正如课程标题写的，在这门课程中你将从头编写大语言模型的所有核心组件，例如 Tokenizer，模型架构，训练优化器，底层算子，训练数据清洗，后训练算法等等。每次作业的 handout 都有四五十页 pdf，相当硬核。如果你想充分吃透大语言模型的所有底层细节，那么非常推荐学习这门课程。
+
+- [CMU 11868: Large Language Model Systems](https://llmsystem.github.io/llmsystem2025spring/): CMU 的大语言模型系统课程，侧重底层系统优化，例如 GPU 加速，分布式训练和推理，以及各种前沿技术。非常适合从事系统领域的同学对这个方向有个全貌性的了解。课表里还包含了一篇我发表的 PD 分离相关的文章，因此私心推荐一下。课程作业的话会让你先实现一个迷你 Pytorch，然后在上面实现各种大语言模型的系统级优化。
+
+- [CMU 11667: Large Language Models: Methods and Applications](https://cmu-llms.org/) 和 [CMU 11711: Advanced NLP](https://www.phontron.com/class/anlp-fall2024/): 和前两门课相比，这两门课更偏重上层算法和应用，而且每节课都列举了很多相关阅读材料，适合对大语言模型发展前沿的各个方向都有个粗糙的认识，如果对某个子领域感兴趣的话再寻着参考资料深入学习。
+
+除了课程以外，还有很多不错的资料作为参考：
+
+- [Awesome-LLM](https://github.com/Hannibal046/Awesome-LLM): 大语言模型相关资料汇总
+- [LLMSys-PaperList](https://github.com/AmberLJC/LLMSys-PaperList): 大语言模型系统相关论文汇总
+- [MLsys-Guide](https://github.com/PKU-DAIR/Starter-Guide/blob/main/docs/systems/Readme.md): 深度学习系统入门指南

docs/深度生成模型/大语言模型/CMU11-667.en.md

@@ -0,0 +1,31 @@
+# CMU11-667: Large Language Models: Methods and Applications
+
+## Course Overview
+
+- University: Carnegie Mellon University  
+- Prerequisites: Solid background in machine learning (equivalent to CMU 10-301/10-601) and natural language processing (equivalent to 11-411/11-611); proficiency in Python and familiarity with PyTorch or similar deep learning frameworks.  
+- Programming Language: Python  
+- Course Difficulty: 🌟🌟🌟🌟  
+- Estimated Study Hours: 100+ hours  
+
+This graduate-level course provides a comprehensive overview of methods and applications of Large Language Models (LLMs), covering a wide range of topics from core architectures to cutting-edge techniques. Course content includes:
+
+1. **Foundations**: Neural network architectures for language modeling, training procedures, inference, and evaluation metrics.  
+2. **Advanced Topics**: Model interpretability, alignment methods, emergent capabilities, and applications in both textual and non-textual domains.  
+3. **System & Optimization Techniques**: Large-scale pretraining strategies, deployment optimization, and efficient training/inference methods.  
+4. **Ethics & Safety**: Addressing model bias, adversarial attacks, and legal/regulatory concerns.
+
+The course blends lectures, readings, quizzes, interactive exercises, assignments, and a final project to offer students a deep and practical understanding of LLMs, preparing them for both research and real-world system development.
+
+**Self-Study Tips**:
+
+- Thoroughly read all assigned papers and materials before each class.  
+- Become proficient with PyTorch and implement core models and algorithms by hand.  
+- Complete the assignments diligently to build practical skills and reinforce theoretical understanding.
+
+## Course Resources
+
+- Course Website: <https://cmu-llms.org/>  
+- Course Videos: Selected lecture slides and materials are available on the website; full lecture recordings may require CMU internal access.  
+- Course Materials: Curated research papers and supplementary materials, with the full reading list available on the course site.  
+- Assignments: Six programming assignments covering data preparation, Transformer implementation, retrieval-augmented generation, model evaluation and debiasing, and training efficiency. Details at <https://cmu-llms.org/assignments/>

docs/深度生成模型/大语言模型/CMU11-667.md

@@ -0,0 +1,31 @@
+# CMU11-667: Large Language Models: Methods and Applications
+
+## 课程简介
+
+- 所属大学：Carnegie Mellon University
+- 先修要求：具备机器学习基础（相当于 CMU 的 10-301/10-601）和自然语言处理基础（相当于 11-411/11-611）；熟练掌握 Python，熟悉 PyTorch 等深度学习框架。
+- 编程语言：Python
+- 课程难度：🌟🌟🌟🌟
+- 预计学时：100 学时以上
+
+该研究生课程全面介绍了大型语言模型（LLM）的方法与应用，涵盖从基础架构到前沿技术的广泛主题。课程内容包括：
+
+1. **基础知识**：语言模型的网络架构、训练、推理和评估方法。
+2. **进阶主题**：模型解释性、对齐方法、涌现能力，以及在语言任务和非文本任务中的应用。
+3. **扩展技术**：大规模预训练技术、模型部署优化，以及高效的训练和推理方法。
+4. **伦理与安全**：模型偏见、攻击方法、法律问题等。
+
+课程采用讲座、阅读材料、小测验、互动活动、作业和项目相结合的方式进行，旨在为学生提供深入理解 LLM 的机会，并为进一步的研究或应用打下坚实基础。
+
+**自学建议**：
+
+- 认真阅读每次课前指定的论文和材料。
+- 熟悉 PyTorch 等深度学习框架，动手实现模型和算法。
+- 扎实完成课程作业。
+
+## 课程资源
+
+- 课程网站：<https://cmu-llms.org/>
+- 课程视频：部分讲座幻灯片和材料可在课程网站获取，完整视频可能需通过 CMU 内部平台访问。
+- 课程教材：精选论文和资料，具体阅读列表详见课程网站。
+- 课程作业：共六次作业，涵盖预训练数据准备、Transformer 实现、检索增强生成、模型比较与偏见缓解、训练效率提升等主题，详情见 <https://cmu-llms.org/assignments/>

docs/深度生成模型/大语言模型/CMU11-711.en.md

@@ -0,0 +1,27 @@
+# CMU 11-711: Advanced Natural Language Processing (ANLP)
+
+## Course Overview
+
+* University: Carnegie Mellon University  
+* Prerequisites: No strict prerequisites, but students should have experience with Python programming, as well as a background in probability and linear algebra. Prior experience with neural networks is recommended.  
+* Programming Language: Python  
+* Course Difficulty: 🌟🌟🌟🌟  
+* Estimated Workload: 100 hours  
+
+This is a graduate-level course covering both foundational and advanced topics in Natural Language Processing (NLP). The syllabus spans word representations, sequence modeling, attention mechanisms, Transformer architectures, and cutting-edge topics such as large language model pretraining, instruction tuning, complex reasoning, multimodality, and model safety. Compared to similar courses, this course stands out for the following reasons:
+
+1. **Comprehensive and research-driven content**: In addition to classical NLP methods, it offers in-depth discussions of recent trends and state-of-the-art techniques such as LLaMa and GPT-4.  
+2. **Strong practical component**: Each lecture includes code demonstrations and online quizzes, and the final project requires reproducing and improving upon a recent research paper.  
+3. **Highly interactive**: Active engagement is encouraged through Piazza discussions, Canvas quizzes, and in-class Q&A, resulting in an immersive and well-paced learning experience.
+
+Self-study tips:
+
+* Read the recommended papers before class and follow the reading sequence step-by-step.  
+* Set up a Python environment and become familiar with PyTorch and Hugging Face, as many hands-on examples are based on these frameworks.
+
+## Course Resources
+
+* Course Website: [https://www.phontron.com/class/anlp-fall2024/](https://www.phontron.com/class/anlp-fall2024/)  
+* Course Videos: Lecture recordings are available on Canvas (CMU login required)  
+* Course Texts: Selected classical and cutting-edge research papers + chapters from *A Primer on Neural Network Models for Natural Language Processing* by Yoav Goldberg  
+* Course Assignments: [https://www.phontron.com/class/anlp-fall2024/assignments/](https://www.phontron.com/class/anlp-fall2024/assignments/)

docs/深度生成模型/大语言模型/CMU11-711.md

@@ -0,0 +1,28 @@
+# CMU 11-711: Advanced Natural Language Processing (ANLP)
+
+## 课程简介
+
+* 所属大学：Carnegie Mellon University
+* 先修要求：无硬性先修要求，但需具备 Python 编程经验，以及概率论和线性代数基础；有神经网络使用经验者更佳。
+* 编程语言：Python
+* 课程难度：🌟🌟🌟🌟
+* 预计学时：100 学时
+
+该课程为研究生级别的 NLP 入门与进阶课程，覆盖从词表征、序列建模，到注意力机制、Transformer 架构，再到大规模语言模型预训练、指令微调与复杂推理、多模态和安全性等前沿主题。与其他同类课程相比，本课程：
+
+1. **内容全面且紧跟最新研究**：除经典算法外，深入讲解近年热门的大模型方法（如 LLaMa、GPT-4 等）。
+2. **实践性强**：每次课配套代码演示与在线小测，学期末项目需复现并改进一篇前沿论文。
+3. **互动良好**：Piazza 讨论、Canvas 测验及现场答疑，学习体验沉浸而有节奏。
+
+自学建议：
+
+* 提前阅读课前推荐文献，跟着阅读顺序循序渐进。
+* 准备好 Python 环境并熟悉 PyTorch/Hugging Face，因为大量实战代码示例基于此。
+* 扎实完成课程作业。
+
+## 课程资源
+
+* 课程网站：[https://www.phontron.com/class/anlp-fall2024/](https://www.phontron.com/class/anlp-fall2024/)
+* 课程视频：课堂讲座录制并上传至 Canvas（需 CMU 帐号登录）
+* 课程教材：各类经典与前沿论文＋Goldberg《A Primer on Neural Network Models for Natural Language Processing》章节阅读
+* 课程作业：[https://www.phontron.com/class/anlp-fall2024/assignments/](https://www.phontron.com/class/anlp-fall2024/assignments/)