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Learning Generative Adversarial Networks.pdf
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What you need for this book
All of the tools, libraries, and datasets used in this book are open
source and available free of charge. Some cloud environments used
in the book offer free trials for evaluation. With this book, and some
adequate exposure to machine learning (or deep learning), the reader
will be able to dive into the creative nature of deep learning through
generative adversarial networks.
You will need to install Python and some additional Python packages
using pip to effectively run the code samples presented in this book.
Who this book is for
The target audience of this book is machine learning experts and data
scientists who want to explore the power of generative models to
create realistic images from unlabeled raw data or noise.
Readers who possess adequate knowledge of machine learning and
neural network, and have exposure to the Python programming
language can use this book to learn how to synthesize images from
text or discover relationships across similar domains automatically and
explore unsupervised domains with deep learning based generative
architecture.
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