This project implements and investigates the Variational Autoencoder on binarized MNIST digits by building a generative model to infer the bottom half of the given binarized MNIST digits conditioned on the top half of these images.

Implementations include:

• Project.toml packages for the Julia environment.
• variational_autoencoder.py Python version.
• loadMNIST.py loading MNIST data in Python.
• example_flux_model.jl example flux model in Julia.
• vae.jl source code in Julia.
• encoder_params.bson final params/weights of trained model.
• decoder_params.bson final params/weights of trained model.
• Julia-Variational-Autoencoder-Final.ipynb the final jupyter notebook project.

Note: this project is part of the assignment from Statistical Methods for Machine Learning II at the Univeristy of Toronto.