Generative Adversarial Networks Cookbook: Over 100 recipes to build generative models using TensorFlow and Keras > PACKT 원서리스트

Implement powerful algorithms using Python to simplify next-generation deep learning About This Book * A recipe-based approach to tackle key challenges of GANs *Build, train, optimize, and deploy GAN applications using TensorFlow and Keras *Use neural network architecture with different types of 2D and 3D data Who This Book Is For This book is for data scientists, machine learning developers, and deep learning practitioners looking for a quick reference to tackle challenges and tasks in the GAN domain. Familiarity with machine learning concepts and working knowledge of Python programming language will help you get the most out of the book. What You Will Learn * Structure a GAN architecture in pseudocode *Understand the common architecture for each of the GAN models you will build *Implement the latest GAN architectures in Python and Keras *Use different datasets to enable neural network functionality in GAN models *Combine different GAN models and learn how to fine-tune them *Produce a model that can make 3D models worth 3D printing *Develop a GAN to learn a different type of action sequence In Detail Developing Generative Adversarial Networks (GANs) is a complex task, and it is often hard to find code that is easy to understand. This book leads you through eight different examples of modern GAN implementation, including CycleGAN, simGAN, DCGAN, and Imitation Learning with GANs. Each chapter builds on a common architecture in Python and Keras to explore increasingly difficult GAN architectures in an easy-to-read format. Generative Adversarial Networks Cookbook starts by covering the different types of GAN architecture to help you understand how the model works. You will learn how to perform key tasks and operations such as creating false and high-resolution images, text to image synthesis, and generating videos with this recipe-based guide. You will also work with use cases such as DCGAN and deepGAN. To get well versed with the working of complex applications, you will take different real-world datasets and put them to use. By the end of this book, you will be equipped to deal with the challenges and issues that you may face while working with GAN models thanks to easy-to-follow code solutions that you can implement right away.