FREE BOOK ô Generative Adversarial Networks Cookbook: Over 100 recipes to build generative models using TensorFlow and Keras æ eBook or Kindle ePUB free

FREE BOOK Û Generative Adversarial Networks Cookbook: Over 100 recipes to build generative models using TensorFlow and Keras õ Implement powerful algorithms using Python to simplify next generation deep learningKey Features 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 D and D data Book Description 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 formatGenerative 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 useBy 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 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 D models worth D printing Develop a GAN to learn a different type of action sequence 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 About the Author Josh Kalin is a Physicist and Roboticist based in the US focused on the intersection of machine learning, robotics, and analytics Josh has degrees in Physics, Mathematics, Computer Science, and Mechanical Engineering with studies in machine learning He s working with Deep Learning, Machine Learning, and Neural Networks for the past five years He s used neural networks on everything from forex data to images His current work is focused on GANs and Adversarial examples for Reinforcement Learning techniques He also works in a research group that uses deep learning and machine learning for manufacturing and autonomous systems deployment Informative preview of commonly used GANs, but unfortunately it reads like a blog post Not worth the 59.99 CAD I paid for it Part of the introductory fodder to each GAN example is a cursory reminder to read the corresponding arXiv paper for with limited to no summary of the key principles inherent to each.