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Key benefits

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

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.

Who is this book 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.

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

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Amazon verified reviews

syeduzzaman khan Mar 01, 2021

The book is well written. The author explained step by step of Deep learning implementation using Python and TensorFlow library. The GAN implementation requires a lot of effort from the scratch. But if you follow the book, you can save a your learning time and model building time. I recommend to buy this book.

Amazon Verified review

MLEngineer Feb 28, 2021

Disclaimer: The publisher asked me to review the book and gave me a review copy. This represents my opinion on the book.The book is well outlined, organized, and easy to use. I read about and implemented, with the help of the book, the Variational Autoencoder to explore a representation/generation space of data at work. As a practical person I liked the book a lot.The book is a great quickstart into representation with neural networks. (I also read it more deeply at times and it is great for that as well. I myself have experience with high-throughput large scale autoencoders with TensorFlow and building Facial Recognition applications. I appreciated this book a lot.)

BB Mar 08, 2021

Clean explanation of the most important techniques! Even advanced topics like StyleGAN were explained very well!

Susie Jun 25, 2021

This book is a simple cookbook approach to building projects with VAEs and GANs. The book comes along with actual TensorFlow (python) code samples, in the form of Jupyter Notebooks (.ipynb). So, it is very helpful to read through the material and quickly work on simple GAN projects. Initial chapters explain concepts in brief as expected and then starts discussing more complex approaches in later chapters. This book is however low on the math behind GANs and does not discuss how to debug GANs when they don't work.Note: Advanced chapters in this book present complex projects which require multi-GPU systems! I'm yet to get to those projects at the time of this review.

Vince S. Feb 07, 2021

The author provided me a free copy and asked me to write a review here.I think overall it is a decent book. It covered most of the state of the art algorithms. Also it strike the right balance of the algorithm intuition and implementation details. There is a complimentary github comes with this book, which is very nice. The code shown in the book is mostly written in tensorflow 2 or keras. I think this book is more like a technical handbook covering many dots rather than a textbook built you foundation and insights.

Hands-On Image Generation with TensorFlow: A practical guide to generating images and videos using deep learning

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