Implement various state-of-the-art architectures, such as GANs and autoencoders, for image generation using TensorFlow 2.x from scratch Key Features Understand the different architectures for image generation, including autoencoders and GANs - Build models that can edit an image of your face, turn photos into paintings, and generate photorealistic images - Discover how you can build deep neural networks with advanced TensorFlow 2.x features Book Description The emerging field of Generative Adversarial Networks (GANs) has made it possible to generate indistinguishable images from existing datasets. With this hands-on book, you'll not only develop image generation skills but also gain a solid understanding of the underlying principles. Starting with an introduction to the fundamentals of image generation using TensorFlow, this book covers Variational Autoencoders (VAEs) and GANs. You'll discover how to build models for different applications as you get to grips with performing face swaps using deepfakes, neural style transfer, image-to-image translation, turning simple images into photorealistic images, and much more. You'll also understand how and why to construct state-of-the-art deep neural networks using advanced techniques such as spectral normalization and self-attention layer before working with advanced models for face generation and editing. You'll also be introduced to photo restoration, text-to-image synthesis, video retargeting, and neural rendering. Throughout the book, you'll learn to implement models from scratch in TensorFlow 2.x, including PixelCNN, VAE, DCGAN, WGAN, pix2pix, CycleGAN, StyleGAN, GauGAN, and BigGAN. By the end of this book, you'll be well versed in TensorFlow and be able to implement image generative technologies confidently. What You Will Learn Train on face datasets and use them to explore latent spaces for editing new faces - Get to grips with swapping faces with deepfakes - Perform style transfer to convert a photo into a painting - Build and train pix2pix, CycleGAN, and BicycleGAN for image-to-image translation - Use iGAN to understand manifold interpolation and GauGAN to turn simple images into photorealistic images - Become well versed in attention generative models such as SAGAN and BigGAN - Generate high-resolution photos with Progressive GAN and StyleGAN Who this book is for The Hands-On Image Generation with TensorFlow book is for deep learning engineers, practitioners, and researchers who have basic knowledge of convolutional neural networks and want to learn various image generation techniques using TensorFlow 2.x. You'll also find this book useful if you are an image processing professional or computer vision engineer looking to explore state-of-the-art architectures to improve and enhance images and videos. Knowledge of Python and TensorFlow will help you to get the best out of this book. "All TensorFlow/Keras, with very readable code examples. Includes a section on StyleGAN, which will come in handy" ... "it's well explained."- Francois Chollet, Google AI, Creator of Keras This book elegantly simplifies and explains the complex mathematics needed to implement image generation algorithms... The book will perfectly guide research engineers and machine learning engineers who usually feel reluctant to step into the challenging avenue of image generation. The book is end-to-end in that it covers implementation of seminal papers in the field all the way till the latest video generation algorithms. With the way the explanations and code are intertwined all along this book, I found it very easy to switch to TensorFlow 2.x from PyTorch I was using for my research work. - Shrinivasan Sankar, VGG Group, University of Oxford When I was approached by the publisher to write a book on GANs, my first thought was "Do people still read books?". There are many online courses, tutorials, and even free code on GitHub repositories, so why do people still want to buy a book to learn AI? After some market research, I found that there weren't any books/blogs/courses that take someone from the basics to state-of-the-art generative models.  While the existing tutorials, courses, and books teach only fairly simple models using toy datasets, the free GitHub code is often difficult for beginners to read. This is worsened by the fact that they use different machine learning frameworks, versions, and coding styles. This makes learning GANs a daunting experience to many. That is the reason that motivated me to write this book. Many book authors   took code from GitHub without really understanding it. I decided to write all models from scratch so that I could feel the frustration first-hand and live to explain them to you. This is the first book with detailed implementation of advanced GANs, including StyleGAN and BigGAN. Soon Yau Cheong is an AI consultant and the founder of Sooner.ai Ltd. With a history of being associated with industry giants such as NVIDIA and Qualcomm,