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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
Author Profile Icon John Hany
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

CUDA – GPU acceleration for fast training and evaluation

The NVIDIA CUDA Toolkit (https://developer.nvidia.com/cuda-toolkit) is a fully optimized parallel computing platform for general-purpose computing on graphics processing units (GPGPU). It allows us to perform scientific computing on NVIDIA graphic cards, including linear algebra, image and video processing, deep learning, and graph analytics. It is used by a lot of commercial and open source software to enable GPU-accelerated computation across different domains. If we look back at the development of deep learning, we should realize that the latest breakthroughs in GANs would have been almost impossible without the help of CUDA and powerful GPUs. Therefore, we highly recommend you try out the experiments in this book on a CUDA-compatible GPU; otherwise, the training time of neural networks could be painfully long...

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