These notes form a concise introductory course on deep generative models. They are based on Stanford CS236, taught by Stefano Ermon and Aditya Grover, and have been written by Aditya Grover, with the help of many students and course staff. The notes are still under construction! Since these notes are brand new, you will find several typos. If you do, please let us know, or submit a pull request with your fixes to our Github repository. You too may help make these notes better by submitting your improvements to us via Github.

  1. Introduction

  2. Autoregressive Models

  3. Variational Autoencoders

  4. Normalizing Flow Models

  5. Generative Adversarial Networks

Contents - Aditya Grover