An Introduction to Variational Autoencoders Using Kerasby@kelseyfoster
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An Introduction to Variational Autoencoders Using Keras

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Variational Autoencoders were invented to accomplish the goal of data generation. Generative models learn a distribution that defines how one feature of a dataset depends on the others. This distribution can then be used to generate *new* data that is similar to the training data. In this tutorial, we’ll explore how VAEs extend their predecessors to address the challenge of data-generation. We'll then train and train a Variational Autooencoder with Keras to understand and visualize how a VAE learns.

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