[ROADMAP] Add stanford cs236 (#904)

* [COURSE] Add Stanford CS236 recommendation

* [COURSE] Add Stanford CS236 English recommendation

docs/深度生成模型/roadmap.en.md

@@ -10,6 +10,8 @@ Recommended courses for learning:
 
 - [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.
 
+- [Stanford CS236: Deep Generative Models](https://deepgenerativemodels.github.io/): A classic Stanford course taught by Stefano Ermon. Starting from Autoregressive Models and Maximum Likelihood Learning, it systematically covers VAEs, Normalizing Flows, GANs, EBMs, Score-based Models, and Diffusion Models. Compared with MIT 6.S184, CS236 takes a more probabilistic modeling perspective and derives recurring concepts such as the ELBO, Change of Variables, and Score Matching from first principles. There is a fair amount of math, but the course progresses naturally. If you want to understand why different generative models are designed the way they are rather than merely knowing how to train a Diffusion Model, this is an excellent course to follow end-to-end.
+
 - [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.

docs/深度生成模型/roadmap.md

@@ -9,6 +9,8 @@
 
 - [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。
 
+- [Stanford CS236: Deep Generative Models](https://deepgenerativemodels.github.io/): Stanford 的经典生成模型课程，由 Stefano Ermon 主讲，从 Autoregressive Model 和 Maximum Likelihood 出发，系统覆盖 VAE、Normalizing Flow、GAN、EBM、Score-based Model 和 Diffusion Model 等主流生成模型。相比 MIT 6.S184，这门课更偏概率建模视角，ELBO、Change of Variables、Score Matching 等常见概念都会从头推导，数学不少但循序渐进。如果你不满足于“会调 Diffusion”，而是想真正理解各种生成模型为什么这样设计，非常推荐把这门课作为主线系统学习。
+
 - [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。