[COURSE] Add CMU 11-785, MIT 6.7960, and NYU DLSP21 (#839)

docs/CS学习规划.en.md

@@ -330,6 +330,14 @@ The popularity of AlphaGo a few years ago brought deep learning to the public ey
 
 Due to the rapid development of deep learning, there are now many research branches. For further in-depth study, consider the following representative courses:
 
+If you want rigorous fundamentals, start with CMU 11-785: it is dense, practical, and has very little filler content. MIT 6.7960 provides broader coverage beyond mainstream LLM topics, including CV-oriented material, and its assignments/projects are feasible for self-learners. NYU DLSP21 is especially notable because it is taught by Yann LeCun, offering a rare public opportunity to follow a full deep learning course from him.
+
+#### Fundamentals and Breadth
+
+- [CMU 11-785: Introduction to Deep Learning](深度学习/CMU11-785.md)
+- [MIT 6.7960: Deep Learning](深度学习/MIT6-7960.md)
+- [NYU DLSP21: NYU Deep Learning Spring 2021](深度学习/NYU-DLSP21.md)
+
 #### Computer Vision
 
 - [UMich EECS 498-007 / 598-005: Deep Learning for Computer Vision](深度学习/EECS498-007.md)

docs/CS学习规划.md

@@ -323,6 +323,16 @@ Berkeley 作为著名开源数据库 postgres 的发源地也不遑多让，[UCB
 
 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:
 
+如果你想系统打牢基础，可以优先看 CMU 11-785：这门课风格非常扎实，内容一点不水，作业也能很好地锻炼训练与调参能力；MIT 6.7960 的覆盖面更广，除了主流 LLM 路线之外也兼顾 CV 等方向，作业和项目对自学者也比较友好；NYU DLSP21 的亮点则是 Yann LeCun 亲自授课，对很多人来说这是非常难得的公开系统课程资源。
+
+#### 综合与基础
+
+[CMU 11-785: Introduction to Deep Learning](深度学习/CMU11-785.md)
+
+[MIT 6.7960: Deep Learning](深度学习/MIT6-7960.md)
+
+[NYU DLSP21: NYU Deep Learning Spring 2021](深度学习/NYU-DLSP21.md)
+
 #### 计算机视觉
 
 [UMich EECS 498-007 / 598-005: Deep Learning for Computer Vision](深度学习/EECS498-007.md)

docs/深度学习/CMU11-785.en.md

@@ -0,0 +1,24 @@
+# CMU 11-785: Introduction to Deep Learning
+
+## Descriptions
+
+- Offered by: CMU
+- Prerequisites: Linear Algebra, Probability, Python Programming, and ML Foundations
+- Programming Languages: Python
+- Difficulty: 🌟🌟🌟🌟🌟
+- Class Hour: ~120 hours
+
+CMU 11-785 is a rigorous, fast-paced deep learning core course with very little filler. It starts from neural network fundamentals and systematically covers CNNs, RNNs, Attention/Transformers, optimization, and generalization.
+
+The workload feels close to graduate-level training: assignments usually require real understanding of model behavior, training details, and experimental methodology. If you want durable deep learning fundamentals (instead of only using high-level APIs), this course is an excellent investment.
+
+## Course Resources
+
+- Course Website: <https://deeplearning.cs.cmu.edu/S26/index.html>
+- Recordings: Lecture recordings are available on course websites (varies by semester)
+- Textbooks: Mainly Lecture Notes / Slides + paper readings
+- Assignments: Multiple programming assignments and a course project (published on course sites)
+
+## Personal Resources
+
+No public personal repository is currently provided for this course.

docs/深度学习/CMU11-785.md

@@ -0,0 +1,24 @@
+# CMU 11-785: Introduction to Deep Learning
+
+## 课程简介
+
+- 所属大学：CMU
+- 先修要求：线性代数、概率论、Python 编程、机器学习基础
+- 编程语言：Python
+- 课程难度：🌟🌟🌟🌟🌟
+- 预计学时：120 小时
+
+CMU 11-785 是一门非常“硬核”的深度学习核心课，整体风格扎实、节奏快、几乎没有“水内容”。课程从神经网络基础出发，逐步覆盖 CNN、RNN、Attention/Transformer、优化与泛化等核心主题，适合想把理论与实践一起打牢的同学。
+
+这门课的学习体验更接近“研究生强度训练”：作业通常不只是套模板跑通，而是要求你理解模型行为、训练细节和实验设计。若你希望建立长期可迁移的深度学习能力（而非只会调几个现成 API），这门课的投入产出比很高。
+
+## 课程资源
+
+- 课程网站：<https://deeplearning.cs.cmu.edu/S26/index.html>
+- 课程视频：课程网站提供 Lecture 录像（不同学期页面会更新）
+- 课程教材：以 Lecture Notes / Slides + 论文阅读为主
+- 课程作业：课程网站发布，通常包含多个编程作业与课程项目
+
+## 资源汇总
+
+暂未提供公开的个人课程笔记与作业仓库。

docs/深度学习/MIT6-7960.en.md

@@ -0,0 +1,24 @@
+# MIT 6.7960: Deep Learning
+
+## Descriptions
+
+- Offered by: MIT
+- Prerequisites: Linear Algebra, Probability/Statistics, and ML Foundations
+- Programming Languages: Python
+- Difficulty: 🌟🌟🌟🌟
+- Class Hour: ~90 hours
+
+A key strength of MIT 6.7960 is breadth. Beyond mainstream LLM/NLP topics, it also includes computer-vision-oriented content, making it a good choice if you want broader coverage rather than a single-track focus.
+
+The course typically combines theory, model design, and application cases. For self-learners with basic foundations, its assignments/projects are practical for advanced training across multiple subfields.
+
+## Course Resources
+
+- Course Website: <https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/>
+- Recordings: Lecture videos and related materials on MIT OCW
+- Textbooks: Lecture Notes + recommended papers
+- Assignments: Assignment and project information on MIT OCW
+
+## Personal Resources
+
+No public personal repository is currently provided for this course.

docs/深度学习/MIT6-7960.md

@@ -0,0 +1,24 @@
+# MIT 6.7960: Deep Learning
+
+## 课程简介
+
+- 所属大学：MIT
+- 先修要求：线性代数、概率统计、机器学习基础
+- 编程语言：Python
+- 课程难度：🌟🌟🌟🌟
+- 预计学时：90 小时
+
+MIT 6.7960 的优势在于覆盖面相对更广：除了主流的 LLM/NLP 主线，也会涉及计算机视觉等任务与方法，适合希望建立“多方向通识”的学习者，而不是只押注单一赛道。
+
+课程通常会把理论、模型设计和应用案例结合起来，帮助你理解“同一套深度学习思想”如何在不同任务中落地。对自学者来说，这门课的作业和项目也很适合做进阶练手。
+
+## 课程资源
+
+- 课程网站：<https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/>
+- 课程视频：MIT OCW 页面提供 Lecture 视频与相关材料
+- 课程教材：Lecture Notes / 讲义 + 推荐论文
+- 课程作业：MIT OCW 页面提供作业与项目信息
+
+## 资源汇总
+
+暂未提供公开的个人课程笔记与作业仓库。

docs/深度学习/NYU-DLSP21.en.md

@@ -0,0 +1,24 @@
+# NYU DLSP21: NYU Deep Learning Spring 2021
+
+## Descriptions
+
+- Offered by: NYU
+- Prerequisites: Linear Algebra, Probability, and Python Programming
+- Programming Languages: Python
+- Difficulty: 🌟🌟🌟🌟
+- Class Hour: ~80 hours
+
+The most distinctive feature of this course is that it is taught by Yann LeCun. For many learners, this is a rare public opportunity to follow a full deep learning course from a top researcher.
+
+Its style balances theory and intuition with a research-oriented perspective. It works especially well as a high-quality follow-up after introductory courses, helping you build better instincts for problem framing and model design.
+
+## Course Resources
+
+- Course Website: <https://atcold.github.io/NYU-DLSP21/>
+- Recordings: Full lecture videos on the course website
+- Textbooks: Lecture Notes / Slides
+- Assignments: Assignments and practical materials on the course website
+
+## Personal Resources
+
+No public personal repository is currently provided for this course.

docs/深度学习/NYU-DLSP21.md

@@ -0,0 +1,24 @@
+# NYU DLSP21: NYU Deep Learning Spring 2021
+
+## 课程简介
+
+- 所属大学：NYU
+- 先修要求：线性代数、概率论、Python 编程
+- 编程语言：Python
+- 课程难度：🌟🌟🌟🌟
+- 预计学时：80 小时
+
+这门课最具辨识度的亮点是由 Yann LeCun 亲自授课。对很多同学而言，这是极少见的公开系统课程资源：可以完整地听到顶级学者从一线研究者视角讲解深度学习。
+
+课程讲解兼顾理论与直觉，整体偏研究导向，适合已完成入门课后继续提升“问题意识”和“建模品味”。把它作为主线课程之外的高质量补充，会非常有价值。
+
+## 课程资源
+
+- 课程网站：<https://atcold.github.io/NYU-DLSP21/>
+- 课程视频：课程网站提供完整 Lecture 视频
+- 课程教材：Lecture Notes / Slides
+- 课程作业：课程网站提供作业与实验材料
+
+## 资源汇总
+
+暂未提供公开的个人课程笔记与作业仓库。

mkdocs.yml

@@ -280,6 +280,9 @@ nav:
   - 深度学习:
       - "Coursera: Deep Learning": "深度学习/CS230.md"
       - "国立台湾大学: 李宏毅机器学习": "深度学习/LHY.md"
+      - "CMU 11-785: Introduction to Deep Learning": "深度学习/CMU11-785.md"
+      - "MIT 6.7960: Deep Learning": "深度学习/MIT6-7960.md"
+      - "NYU DLSP21: NYU Deep Learning Spring 2021": "深度学习/NYU-DLSP21.md"
       - "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"
       - "Stanford CS224n: Natural Language Processing": "深度学习/CS224n.md"