Introduction to Variational Autoencoders Using Keras

The text discusses discriminative models in machine learning, which learn a distribution that defines how one feature of a dataset depends on the others. It also introduces Variational Autoencoders (VAEs), a class of Deep Learning architectures used for data generation. VAEs were invented to accomplish the goal of data generation and have received great attention due to both their impressive results and underlying simplicity. The text provides an overview of how VAEs work, including training on different images and characterizing the latent space as a feature landscape. It also guides readers through building a Variational Autoencoder with Keras for generating images of clothing using the MNIST Fashion dataset.