Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/timudk/gmin
Jupyter notebooks for generative models using NumPy
https://github.com/timudk/gmin
Last synced: 4 days ago
JSON representation
Jupyter notebooks for generative models using NumPy
- Host: GitHub
- URL: https://github.com/timudk/gmin
- Owner: timudk
- License: mit
- Created: 2019-08-20T14:55:27.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2019-09-04T13:05:08.000Z (over 5 years ago)
- Last Synced: 2024-12-22T14:26:49.855Z (about 2 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 173 KB
- Stars: 1
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 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)