Chapter 2. Intro to generative modeling with autoencoders

 

This chapter covers

  • Encoding data into a latent space (dimensionality reduction) and subsequent dimensionality expansion
  • Understanding the challenges of generative modeling in the context of a variational autoencoder
  • Generating handwritten digits by using Keras and autoencoders
  • Understanding the limitations of autoencoders and motivations for GANs

I dedicate this chapter to my grandmother, Aurelie Langrova, who passed away as we were finishing the work on it. She will be missed dearly.

Jakub

You might be wondering why we chose to include this chapter in the book. There are three core reasons:

2.1. Introduction to generative modeling

2.2. How do autoencoders function on a high level?

2.3. What are autoencoders to GANs?

2.4. What is an autoencoder made of?

2.5. Usage of autoencoders

2.6. Unsupervised learning

2.6.1. New take on an old idea

2.6.2. Generation using an autoencoder

2.6.3. Variational autoencoder

2.7. Code is life

2.8. Why did we try aGAN?

Summary

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