How to Implement a Variational Autoencoder in Python and Keras

Implement a Variational Autoencoder (VAE) using Python, TensorFlow, and Keras in this comprehensive 33-minute tutorial. Dive into the step-by-step process of building a VAE, starting with renaming the class and disabling eager execution. Learn how to modify the encoder bottleneck, update the loss function, and pass it to the compile method. Explore the VAE architecture, train the model, and analyze generated data. Visualize the latent space to gain insights into the VAE's performance. Access the accompanying code on GitHub for hands-on practice. Perfect for machine learning enthusiasts and developers looking to expand their knowledge of generative models and deep learning techniques.

Syllabus

Intro + agenda
Renaming class + disabling eager execution
Modifying the encoder bottleneck
Updating the loss function
Passing the loss function to compile
Checking the VAE architecture
Training the VAE
Analysis of generated data
Visualising the latent space
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