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Python Deep Learning

You're reading from   Python Deep Learning Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow

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Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781789348460
Length 386 pages
Edition 2nd Edition
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Authors (5):
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Gianmario Spacagna Gianmario Spacagna
Author Profile Icon Gianmario Spacagna
Gianmario Spacagna
Daniel Slater Daniel Slater
Author Profile Icon Daniel Slater
Daniel Slater
Valentino Zocca Valentino Zocca
Author Profile Icon Valentino Zocca
Valentino Zocca
Peter Roelants Peter Roelants
Author Profile Icon Peter Roelants
Peter Roelants
Ivan Vasilev Ivan Vasilev
Author Profile Icon Ivan Vasilev
Ivan Vasilev
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Toc

Table of Contents (12) Chapters Close

Preface 1. Machine Learning - an Introduction 2. Neural Networks FREE CHAPTER 3. Deep Learning Fundamentals 4. Computer Vision with Convolutional Networks 5. Advanced Computer Vision 6. Generating Images with GANs and VAEs 7. Recurrent Neural Networks and Language Models 8. Reinforcement Learning Theory 9. Deep Reinforcement Learning for Games 10. Deep Learning in Autonomous Vehicles 11. Other Books You May Enjoy

Generating Images with GANs and VAEs

"What I cannot create, I do not understand."- Richard Feynman

This quote is often cited in the same sentence as generative models, and for good reason. In the previous two chapters (Chapter 4, Computer Vision with Convolutional Networks and Chapter 5, Advanced Computer Vision), we focused on supervised computer vision problems, such as classification and object detection. Now, we'll discuss how to create new images with the help of unsupervised neural networks. After all, it's a lot better knowing that you don't need labeled data. More specifically, we'll talk about generative models.

This chapter will cover the following topics:

  • Intuition and justification of generative models
  • Variational autoencoders
  • Generative Adversarial networks
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