add nav

* Update Git.md

* Update Git.en.md

* Update Git.en.md

* Update Git.md

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

@@ -21,4 +21,4 @@ Karpathy holds a **PhD from Stanford University**, where he worked on convolutio
 - **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.  
+For more information, watch the full playlist on YouTube.  

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

@@ -21,4 +21,4 @@ Karpathy 拥有 **斯坦福大学博士学位**，师从 **Fei-Fei Li（李飞
 - **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。  
+更多信息请访问 YouTube 观看完整课程视频。  

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：[CMU15418](http://15418.courses.cs.cmu.edu/spring2016/lectures), [CS149](https://youtube.com/playlist?list=PLoROMvodv4rMp7MTFr4hQsDEcX7Bx6Odp&si=txtQiRDZ9ZZUzyRn)
+- 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/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/机器学习系统/CMU10-414.en.md

@@ -26,3 +26,5 @@ Instructors [Zico Kolter](https://zicokolter.com/) and [Tianqi Chen](https://tqc
 ## 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/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

@@ -6,6 +6,8 @@ In fact, LLMs are just one branch of deep generative models. Other types such as
 
 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.

docs/深度生成模型/roadmap.md

@@ -5,6 +5,8 @@
 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。

docs/编译原理/SJTU-Compilers.en.md

@@ -16,8 +16,8 @@ In this course, you will learn how to use frameworks such as `flexc++`, `Bisonc+
 
 - Course Website: <https://ipads.se.sjtu.edu.cn/courses/compilers/index.shtml>
 - Slides: See the course website
-- Framework Code: See GitHub
-- Course Textbook: "Tiger Book" (Modern Compiler Implementation in C)
+- Framework Code: <https://ipads.se.sjtu.edu.cn:2020/compilers-2024/compilers-2024> (If unable to access, you can use other similar and maintained repositories, such as <https://gitee.com/east-china-normal-university_ttb_cs/tiger-compiler-25sp>)
+- Course Textbook: "Tiger Book" (Modern Compiler Implementation in C) <https://ipads.se.sjtu.edu.cn/courses/compilers/textbook/TigerBook-English.pdf>
 - 2 Quizzes + 6 Labs
     - Lab 1: Straight-line Program Interpreter
     - Lab 2: Lexical Analysis

docs/编译原理/SJTU-Compilers.md

@@ -8,7 +8,7 @@
 - 课程难度：🌟🌟🌟🌟
 - 预计学时：150 小时
 
-上海交通大学的编译原理课程旨在实现一个 Tiger 语言的编译器。在这门课上你可以学习到词法分析、文法分析、予以分析、逃逸分析、活动记录（栈帧）、LLVM IR、活跃分析、寄存器分配、垃圾收集、面向对象、函数式程序等众多话题。和北大的编译原理课程相似，该课程给予了你极大的自由度，测试程序只会对你输出的汇编代码的正确性进行测试，而不会对你编译器的具体设计做任何限制。你需要从一个个空文件中一步步构建出属于你自己的编译器。
+上海交通大学的编译原理课程旨在实现一个 Tiger 语言的编译器。在这门课上你可以学习到词法分析、文法分析、语义分析、逃逸分析、活动记录（栈帧）、LLVM IR、活跃分析、寄存器分配、垃圾收集、面向对象、函数式程序等众多话题。和北大的编译原理课程相似，该课程给予了你极大的自由度，测试程序只会对你输出的汇编代码的正确性进行测试，而不会对你编译器的具体设计做任何限制。你需要从一个个空文件中一步步构建出属于你自己的编译器。
 
 在这门课上你将学到`flexc++`、`Bisonc++`、`LLVM`等框架的使用方法，并在练习过程中加强自己的调试能力。
 
@@ -20,8 +20,8 @@
 
 - 课程网站：<https://ipads.se.sjtu.edu.cn/courses/compilers/index.shtml>
 - 课件：参见课程网站
-- 框架代码：参见 GitHub
-- 课程教材：虎书（Modern Compiler Implementation in C）
+- 框架代码：<https://ipads.se.sjtu.edu.cn:2020/compilers-2024/compilers-2024>（如果无法打开，可以使用其他相同且正在维护的仓库，例如<https://gitee.com/east-china-normal-university_ttb_cs/tiger-compiler-25sp>）
+- 课程教材：虎书（Modern Compiler Implementation in C）<https://ipads.se.sjtu.edu.cn/courses/compilers/textbook/TigerBook-Chinese.pdf>
 - 2 次Quiz + 6 个Lab
     - Lab 1: Straight-line Program Interpreter
     - Lab 2: Lexical Analysis

mkdocs.yml

@@ -286,6 +286,7 @@ nav:
       - "UCB CS285: Deep Reinforcement Learning": "深度学习/CS285.md"
   - 深度生成模型:
       - "学习路线图": "深度生成模型/roadmap.md"
+      - "MIT 6.S184: Generative AI with Stochastic Differential Equations": "深度生成模型/MIT6.S184.md"
       - "大语言模型":
         - "CMU 11-868: Large Language Model System": "深度生成模型/大语言模型/CMU11-868.md"
         - "CMU 11-667: Large Language Models: Methods and Applications": "深度生成模型/大语言模型/CMU11-667.md"