Deep Generative Models - XCS236
Table of Contents
Lecture Videos #
- Lecture Topics (YouTube Playlist):
- Lecture 1 - Introduction
- Lecture 2 - Background
- Lecture 3 - Autoregressive Models
- Lecture 4 - Maximum Likelihood Learning
- Lecture 5 - VAEs
- Lecture 6 - VAEs
- Lecture 7 - Normalizing Flows
- Lecture 8 - Normalizing Flows
- Lecture 9 - GANs
- Lecture 10 - GANs
- Lecture 11 - Energy Based Models
- Lecture 12 - Energy Based Models
- Lecture 13 - Score Based Models
- Lecture 14 - Energy Based Models
- Lecture 15 - Evaluation of Generative Models
- Lecture 16 - Score Based Diffusion Models
- Lecture 17 - Discrete Latent Variable Models
- Lecture 18 - Diffusion Models for Discrete Data
Additional Resources #
- Course Webpage
- Lecture Notes
- Videos:
- Books:
- Deep Learning - Ian Goodfellow, Yoshua Bengio, and Aaron Courville (PDF Download)
- Probabilistic Machine Learning: Advanced Topics - Kevin Patrick Murphy, p771–924 (IV Generation; Chapter 21 – 26) (Info | PDF Download)
My Notes #
- Covered Topics (View PDF):
- Introduction & Background
- Autoregressive Models (AR)
- Maximum Likelihood Learning (MLE)
- Latent Variable Models (VAEs)
- Normalizing Flows
- Generative Adversarial Networks (GANs)
- Energy Based Models (EBMs) (N/A)
- Score Based Models (N/A)
- Score Based Diffusion Models (N/A)
- Discrete Latent Variable Models (N/A)
- Diffusion Models for Discrete Data (N/A)
- Evaluation of Generative Models (N/A)