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https://github.com/timudk/gmin

Jupyter notebooks for generative models using NumPy
https://github.com/timudk/gmin

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Jupyter notebooks for generative models using NumPy

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# Generative models in NumPy

This repository contains implementations of generative models using NumPy.

## Available implementations
1. Naive Bayes (classifier)
2. Boltzmann machine
3. Restricted Boltzmann machine

## In progress
1. GAN

## Motivation
In my opinion, the best way to grasp a new generative model is by implementing it in NumPy. Only by doing so, one can understand the full picture of (probabilisitc) model assumptions, optimization and sampling.

## Sources of inspiration
1. [Danilo Rezende's slides on deep generative models](https://docs.google.com/presentation/d/e/2PACX-1vSwRVxRHDarUx2mwXrsrlrtdTVTyEiFkWjJ9TvJ5ad6gbB3PDZSgD9yHAUiB6DcO1zP7LXBpxzc0SzC/pub?start=true&loop=true&delayms=10000&slide=id.gd9c453428_0_16)
2. Papers on generative models:
* Introducing GANs: [http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf](http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf)
* Introducing Variational autoencoders: [Auto-Encoding Variational Bayes](https://arxiv.org/pdf/1312.6114.pdf)
* [A Practical Guide to Training Restricted Boltzmann Machines](https://www.cs.toronto.edu/~hinton/absps/guideTR.pdf)
* [Stochastic Backpropagation and Approximate Inferencein Deep Generative Models](https://arxiv.org/pdf/1401.4082.pdf)
* Introducing normalizing flows: [Variational Inference with Normalizing Flows](https://arxiv.org/pdf/1505.05770.pdf)