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title: Contents
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These notes form a concise introductory course on deep generative models.
They are based on Stanford [CS236](https://deepgenerativemodels.github.io/), taught by [Stefano Ermon](http://cs.stanford.edu/~ermon/) and [Aditya Grover](http://aditya-grover.github.io/), and have been written by [Aditya Grover](http://aditya-grover.github.io/), with the [help](https://github.com/deepgenerativemodels/notes/commits/master) of many students and course staff.
{% marginnote 'mn-id-whatever' '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](https://github.com/deepgenerativemodels/notes).'%}
You too may help make these notes better by submitting your improvements to us via [Github](https://github.com/deepgenerativemodels/notes).


1. [Introduction](introduction/)

2. [Autoregressive Models](autoregressive/)

3. [Variational Autoencoders](vae/) 

4. [Normalizing Flow Models](flow/) 

5. [Generative Adversarial Networks](gan/)