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Hands-On Generative Adversarial Networks with PyTorch 1.x

You're reading from   Hands-On Generative Adversarial Networks with PyTorch 1.x Implement next-generation neural networks to build powerful GAN models using Python

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Product type Paperback
Published in Dec 2019
Publisher Packt
ISBN-13 9781789530513
Length 312 pages
Edition 1st Edition
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Authors (2):
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John Hany John Hany
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John Hany
Greg Walters Greg Walters
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Greg Walters
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Table of Contents (15) Chapters Close

Preface 1. Section 1: Introduction to GANs and PyTorch
2. Generative Adversarial Networks Fundamentals FREE CHAPTER 3. Getting Started with PyTorch 1.3 4. Best Practices for Model Design and Training 5. Section 2: Typical GAN Models for Image Synthesis
6. Building Your First GAN with PyTorch 7. Generating Images Based on Label Information 8. Image-to-Image Translation and Its Applications 9. Image Restoration with GANs 10. Training Your GANs to Break Different Models 11. Image Generation from Description Text 12. Sequence Synthesis with GANs 13. Reconstructing 3D models with GANs 14. Other Books You May Enjoy

Fundamentals of machine learning

To introduce how GANs work, let's use an analogy:

A long, long time ago, there were two neighboring kingdoms on an island. One was called Netland, and the other was called Ganland. Both kingdoms produced fine wine, armor, and weapons. In Netland, the king demanded that the blacksmiths who specialized in making armor worked at the east corner of the castle, while those who made swords worked at the west side so that the lords and knights could choose the best equipment the kingdom had to offer. The king of Ganland, on the other hand, put all of the blacksmiths in the same corner and demanded that the armor makers and sword makers should test their work against each other every day. If a sword broke through the armor, the sword would sell at a good price and the armor would be melted and reforged. If it didn't, the sword would be remade...
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Hands-On Generative Adversarial Networks with PyTorch 1.x
Published in: Dec 2019
Publisher: Packt
ISBN-13: 9781789530513
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