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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 FREE CHAPTER
2. Generative Adversarial Networks Fundamentals 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

What's new in PyTorch 1.3?

PyTorch (https://pytorch.org) is an open source machine learning platform for Python. It is specifically designed for deep learning applications, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), and it includes extensive layer definitions for these applications. It has built-in tensor operations that are designed to be used in the same way as NumPy arrays, and they are also optimized to run on GPUs for fast computation. It provides an automatic computational graph scheme so that you won't need to calculate derivatives by hand.

After around 3 years of development and improvements, PyTorch has finally reached its newest milestone, version 1.3! What comes with it is a big package of new features and new functionalities. Don't worry about whether you'll have to...

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