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Hands-On Generative Adversarial Networks with Keras

You're reading from   Hands-On Generative Adversarial Networks with Keras Your guide to implementing next-generation generative adversarial networks

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781789538205
Length 272 pages
Edition 1st Edition
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Author (1):
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Rafael Valle Rafael Valle
Author Profile Icon Rafael Valle
Rafael Valle
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Table of Contents (14) Chapters Close

Preface 1. Section 1: Introduction and Environment Setup
2. Deep Learning Basics and Environment Setup FREE CHAPTER 3. Introduction to Generative Models 4. Section 2: Training GANs
5. Implementing Your First GAN 6. Evaluating Your First GAN 7. Improving Your First GAN 8. Section 3: Application of GANs in Computer Vision, Natural Language Processing, and Audio
9. Progressive Growing of GANs 10. Generation of Discrete Sequences Using GANs 11. Text-to-Image Synthesis with GANs 12. TequilaGAN - Identifying GAN Samples 13. Whats next in GANs

Deep learning environment setup

In this section, we will provide instructions on how to set up a Python deep learning programming environment that will be used throughout the book. We will start with Anaconda, which makes package management and deployment easy, and NVIDIA's CUDA Toolkit and cuDNN, which make training and inference in deep learning models quick. There are several compute cloud services, like Amazon Web Services (AWS), that provide ready to use deep learning environments with NVIDIA GPUs.

Installing Anaconda and Python

Anaconda is a free and open source efficient distribution that provides easy package management and deployment for the programming languages R and Python. Anaconda focuses on data science...

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Hands-On Generative Adversarial Networks with Keras
Published in: May 2019
Publisher: Packt
ISBN-13: 9781789538205
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