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49 changes: 48 additions & 1 deletion README.md
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Expand Up @@ -22,11 +22,17 @@ At DAIR.AI we ❤️ open AI education. In this repo, we index and organize some
- [Introduction to Deep Learning (MIT)](#introduction-to-deep-learning)
- [CMU Introduction to Deep Learning (11-785)](#cmu-introduction-to-deep-learning-11-785)
- [Deep Learning: CS 182](#deep-learning-cs-182)
- [Deep Unsupervised Learning](#deep-unsupervised-learning)
- [NYU Deep Learning SP21](#nyu-deep-learning-sp21)
- [Foundation Models](#foundation-models)
- [Deep Learning (Tübingen)](#deep-learning-Tübingen)

**Deep Generative Models**

- [Deep Unsupervised Learning](#deep-unsupervised-learning)
- [Deep Generative Models (Cornell)](#deep-generative-models-cornell)
- [Deep Generative Models (Stanford)](#deep-generative-models-stanford)
- [Generative AI with Stochastic Differential Equations](#mit-6s184-generative-ai-with-stochastic-differential-equations)

**Scientific Machine Learning**

- [Parallel Computing and Scientific Machine Learning](#parallel-computing-and-scientific-machine-learning)
Expand Down Expand Up @@ -886,6 +892,47 @@ This is course is from Peter Abbeel and covers a review on reinforcement learnin

🔗 [Link to Course](https://www.youtube.com/playlist?list=PLwRJQ4m4UJjNBPJdt8WamRAt4XKc639wF) 🔗 [Link to Materials](https://people.eecs.berkeley.edu/~pabbeel/cs287-fa19/)

## Deep Generative Models (Cornell)

This is course is from Volodymyr Kuleshov and explores the foundational probabilistic principles of generative models, their learning algorithms, and popular model families.

- Autoregressive Models
- Latent Variable Models
- Normalizing Flows
- Generative Adversarial Networks
- Energy-Based Models
- Diffusion Models
- ...

🔗 [Link to Course](https://youtube.com/playlist?list=PL2UML_KCiC0UPzjW9BjO-IW6dqliu9O4B&si=tPT7LejqICnnV8f7) 🔗 [Link to Materials](https://kuleshov-group.github.io/dgm-website/)

## Deep Generative Models (Stanford)

This is course is from Stefano Ermon and covers the probabilistic foundations and learning algorithms for deep generative models.

- Variational Autoencoders
- Generative Adversarial Networks
- Autoregressive Models
- Normalizing Flow Models
- Energy-Based Models
- Score-Based Models
- ...

🔗 [Link to Course](https://youtube.com/playlist?list=PLoROMvodv4rPOWA-omMM6STXaWW4FvJT8) 🔗 [Link to Materials](https://deepgenerativemodels.github.io/)

## MIT 6.S184: Generative AI with Stochastic Differential Equations

This course aims to build up the mathematical framework underlying Flow Matching and Diffusion Models from first principles.

- Flow and Diffusion Models
- Constructing a Training Target
- Training Flow and Diffusion Models
- Building an Image Generator
- Generative Robotics
- Generative Protein Design

🔗 [Link to Course](https://www.youtube.com/playlist?list=PL57nT7tSGAAUDnli1LhTOoCxlEPGS19vH) 🔗 [Link to Materials](https://diffusion.csail.mit.edu/)

---

Reach out on [Twitter](https://twitter.com/omarsar0) if you have any questions.
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