How to use this book
There are many online tutorials available teaching the basics of GANs. However, the models tend to be rather simple and suitable only for toy datasets. At the other end of the spectrum, there are also free codes available for state-of-the-art models to generate realistic images. Nevertheless, the code tends to be complex, and the lack of explanation makes it difficult for beginners to understand. Many of the “Git cloners” who downloaded the codes had no clue how to tweak the models to make them work for their applications. This book aims to bridge that gap.
We will start with learning the basic principles and immediately implement the code to put them to the test. You'll be able to see the result of your work instantly. All the necessary code to build a model is laid bare in a single Jupyter notebook. This is to make it easier for you to go through the flow of the code and to modify and test the code in an interactive manner. I believe writing from scratch is the best way to learn and master deep learning. There are between one to three models in each chapter, and we will write all of them from scratch. When you finish this book, not only will you be familiar with image generation but you will also be an expert in TensorFlow 2.
The chapters are arranged in roughly chronological order of the history of GANs, where the chapters may build upon knowledge from previous chapters. Therefore, it is best to read the chapters in order, especially the first three chapters, which cover the fundamentals. After that, you may jump to chapters that interest you more. Should you feel confused by the acronyms during the reading, you can refer to the summary of GAN techniques listed in the last chapter.
