This repository is aimed to practice with Variatonial Autoencoders to model complex distributions and making generative models.
This repository is based in the following paper: Tutorial on Variational Autoencoders. That paper offers a repository where we can find a code for a VAE example for the MNIST dataset. Nevertheless, in this repository we'll build a VAE class only for practicing our coding abiities and get a deeper understanding. After that, we'll compare our results with a VAE coded with packages such as caffe or PyTorch. Additionaly, we will add a more mathematical explanaiton of the Variational Bayesian theory and how is used in this models.
I hope this repository helps anyoene in their path on untherstanding Variational Bayesian models and VAEs.