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Deep Learning Basics and Environment Setup

In this chapter, we offer you essential knowledge for building and training deep learning models, including Generative Adversarial Networks (GANs). We are going to explain the basics of deep learning, starting with a simple example of a learning algorithm based on linear regression. We will also provide instructions on how to set up a deep learning programming environment using Python and Keras. We will also talk about the importance of computing power in deep learning; we are going to describe guidelines to fully take advantage of NVIDIA GPUs by maximizing the memory footprint, enabling the CUDA Deep Neural Network library (cuDNN), and eventually using distributed training setups with multiple GPUs. Finally, in addition to installing the libraries that will be necessary for upcoming projects in this book, you will test your installation...

Key benefits

Discover various GAN architectures using a Python and Keras library

Understand how GAN models function with the help of theoretical and practical examples

Apply your learnings to become an active contributor to open source GAN applications

Description

Generative Adversarial Networks (GANs) have revolutionized the fields of machine learning and deep learning. This book will be your first step toward understanding GAN architectures and tackling the challenges involved in training them. This book opens with an introduction to deep learning and generative models and their applications in artificial intelligence (AI). You will then learn how to build, evaluate, and improve your first GAN with the help of easy-to-follow examples. The next few chapters will guide you through training a GAN model to produce and improve high-resolution images. You will also learn how to implement conditional GANs that enable you to control characteristics of GAN output. You will build on your knowledge further by exploring a new training methodology for progressive growing of GANs. Moving on, you'll gain insights into state-of-the-art models in image synthesis, speech enhancement, and natural language generation using GANs. In addition to this, you'll be able to identify GAN samples with TequilaGAN. By the end of this book, you will be well-versed with the latest advancements in the GAN framework using various examples and datasets, and you will have developed the skills you need to implement GAN architectures for several tasks and domains, including computer vision, natural language processing (NLP), and audio processing. Foreword by Ting-Chun Wang, Senior Research Scientist, NVIDIA

What you will learn

Discover how GANs work and the advantages and challenges of working with them

Control the output of GANs with the help of conditional GANs, using embedding and space manipulation

Apply GANs to computer vision, natural language processing (NLP), and audio processing

Understand how to implement progressive growing of GANs

Use GANs for image synthesis and speech enhancement

Explore the future of GANs in visual and sonic arts

Implement pix2pixHD to turn semantic label maps into photorealistic images

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Frequently bought together

Generative Adversarial Networks Cookbook

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Hands-On Generative Adversarial Networks with Keras

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Generative Adversarial Networks Projects

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Total € 106.97 Stars icon

Tobsi Hausi Jun 08, 2019

0 Stars.I expected I would learn how to build my own model.You only use the examples from GitHub, and these don't work.

Amazon Verified review

Mike H. May 29, 2019

The book holds promise but suffers from several major issues. First of all is the quality is the editorial. This author LOVES their commas. They use what seems like an average of three commas per sentence making those sentences unnecessarily long and often difficult to follow.My other complaint is that the beginning of the book states, in several places, that you only need a general, rudimentary understanding of python as a prerequisite, then launches into graduate level mathematics throughout the entire first half of the book. There is only an exceedingly brief overview of introductory NN concepts like in other books I've read, not enough for someone just starting out to grasp the concepts. This sort of negates the author's insistence in the beginning that you don't need much experience or advanced education in the field to understand the concepts presented.None of the information provided in the book is immediately applicable and it is written for the most part as if lifted straight out of an academic journal. So much of this book is written like that: terribly academic, boring to read, hard to follow, and not as practical as others in this subject (I've read over a dozen on this topic alone).There are also several misprints leading to certain formulas being unreadable and several typos I've found in the text. The code examples are not the easiest to follow compared to other books, even for an experienced developer like myself, because of the way they are presented and described (most of the time there is actually very little explanation as to what is going on in the code). For example, there is no (or very little) description as to why certain things are handled and instantiated in a certain way as presented.Lastly, the graphs and images reference colors in certain segments but they are all printed in black and white - this is a common problem with books published by Pact I've found and not necessarily the author's fault.Overall, it is a hard book to get through, even for this type of material which can be very technical. It is dry, formulaic, academic, and not nearly as practical or easy to follow as others I've read. It feels more like a graduate thesis sometimes than a practical resource for developers and people just starting out in this field. There are far better, and more practical, options available out there for those of you who haven't yet completed a Master's degree in mathematics.

Hands-On Generative Adversarial Networks with Keras: Your guide to implementing next-generation generative adversarial networks

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Hands-On Generative Adversarial Networks with Keras

Deep Learning Basics and Environment Setup

Deep learning basics

Deep learning environment setup

Installing Anaconda and Python

The deep learning environment test

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