This repository contains two Jupyter notebooks that provide comprehensive tutorials on implementing an Autoencoder and a Variational Autoencoder using the MNIST dataset.
Notebooks Included:
- Autoencoder (AE) Tutorial: This notebook guides you through the basics of building and training an Autoencoder, demonstrating how to use it for data compression and reconstruction with the MNIST dataset
- Variational Autoencoder (VAE) Tutorial: This notebook covers the implementation of a Variational Autoencoder, based on the Autoencoder example.
These tutorials were implemented to better understand the concepts and applications of Autoencoders and Variational Autoencoders, providing hands-on experience with these powerful neural network architectures.