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Advanced Deep Learning with TensorFlow 2 and Keras

You're reading from   Advanced Deep Learning with TensorFlow 2 and Keras Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781838821654
Length 512 pages
Edition 2nd Edition
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Author (1):
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Rowel Atienza Rowel Atienza
Author Profile Icon Rowel Atienza
Rowel Atienza
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Table of Contents (16) Chapters Close

Preface 1. Introducing Advanced Deep Learning with Keras 2. Deep Neural Networks FREE CHAPTER 3. Autoencoders 4. Generative Adversarial Networks (GANs) 5. Improved GANs 6. Disentangled Representation GANs 7. Cross-Domain GANs 8. Variational Autoencoders (VAEs) 9. Deep Reinforcement Learning 10. Policy Gradient Methods 11. Object Detection 12. Semantic Segmentation 13. Unsupervised Learning Using Mutual Information 14. Other Books You May Enjoy
15. Index

1. An Overview of GANs

Before we move into the more advanced concepts of GANs, let's start by going over GANs and introducing the underlying concepts behind them. GANs are very powerful; this simple statement is proven by the fact that they can generate new human faces that are not of real people by performing latent space interpolations.

The advanced features of GANs can be seen in these YouTube videos:

The videos that show how GANs can be utilized to produce realistic faces demonstrate how powerful they can be. This topic is much more advanced than anything we've looked at before in this book. For example, the above videos demonstrate things that can't be accomplished easily by autoencoders, which we covered in Chapter 3, Autoencoders.

GANs are able to learn how to model the input...

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