Аннотация

Welcome to Generative Adversarial Networks with Python. Generative Adversarial Networks, or GANs for short, are a deep learning technique for training generative models. GANs are most commonly used for the generation of synthetic images for a specif i c domain that are dif f erent and practically indistinguishable from other real images. The study and application of GANs are only a few years old, yet the results achieved have been nothing short of remarkable. Because the f i eld is so young, it can be challenging to know how to get started, what to focus on, and how to best use the available techniques. This book is designed to teach you step-by-step how to bring the best and most promising aspects of the exciting f i eld of Generative Adversarial Networks to your own projects.
Who Is This Book For?
Before we get started, let’s make sure you are in the right place. This book is for developers that know some deep learning. Maybe you want or need to start using Generative Adversarial Networks on your research project or on a project at work. This guide was written to help you do that quickly and ef f i ciently by compressing years of knowledge and experience into a laser-focused course of hands-on tutorials. The lessons in this book assume a few things about you, such as:
• You know your way around basic Python for programming.
• You know your way around basic NumPy for array manipulation.
• You know your way around basic Keras for deep learning.
For some bonus points, perhaps some of the below criteria apply to you. Don’t panic if they don’t.
• You may know how to work through a predictive modeling problem end-to-end.
• You may know a little bit of computer vision, such as convolutional neural networks.
This guide was written in the top-down and results-f i rst machine learning style that you’re used to from MachineLearningMastery.com.