VAEs are a type of convolutional neural network used for converting data from a high-dimensional space into a low-dimensional one, and then reconstructing it.
In this project we'll see how to build a variational autoencoder (VAE) from scratch in Keras. The models for the encoder, decoder, and the full VAE are saved and then loaded for testing.
For a more detailed breakdown of the code, check out How to Build a Variational Autoencoder in Keras on the blog.
