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Hands-On Image Generation with TensorFlow
Hands-On Image Generation with TensorFlow

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

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Profile Icon Soon Yau Cheong
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€18.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.7 (6 Ratings)
Paperback Dec 2020 306 pages 1st Edition
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€32.99
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€41.99
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Arrow left icon
Profile Icon Soon Yau Cheong
Arrow right icon
€18.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.7 (6 Ratings)
Paperback Dec 2020 306 pages 1st Edition
eBook
€32.99
Paperback
€41.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€32.99
Paperback
€41.99
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Renews at €18.99p/m

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Hands-On Image Generation with TensorFlow

Chapter 1: Getting Started with Image Generation Using TensorFlow

This book focuses on generating images and videos using unsupervised learning with TensorFlow 2. We assume that you have prior experience in using modern machine learning frameworks, such as TensorFlow 1, to build image classifiers with Convolutional Neural Networks (CNNs). Therefore, we will not be covering the basics of deep learning and CNNs. In this book, we will mainly use high level Keras APIs in TensorFlow 2, which is easy to learn. Nevertheless, we assume that you have no prior knowledge of image generation, and we will go through all that is needed to help you get started with it. The first aspect that you need to know about is probability distribution.

Probability distribution is fundamental in machine learning and it is especially important in generative models. Don't worry, I assure you that there aren't any complex mathematical equations in this chapter. We will first learn what probability is and how to use it to generate faces without using any neural networks or complex algorithms.

That's right: with the help of only basic math and NumPy code, you'll learn how to create a probabilistic generative model. Following that, you will learn how to use TensorFlow 2 to build a PixelCNN model in order to generate handwritten digits. This chapter is packed with useful information; you will need to read this chapter before jumping to any other chapters.

In this chapter, we are going to cover the following main topics:

  • Understanding probabilities
  • Generating faces with a probabilistic model
  • Building a PixelCNN model from scratch
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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

Product Details

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Publication date : Dec 24, 2020
Length: 306 pages
Edition : 1st
Language : English
ISBN-13 : 9781838826789
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Product Details

Publication date : Dec 24, 2020
Length: 306 pages
Edition : 1st
Language : English
ISBN-13 : 9781838826789
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Table of Contents

14 Chapters
Section 1: Fundamentals of Image Generation with TensorFlow Chevron down icon Chevron up icon
Chapter 1: Getting Started with Image Generation Using TensorFlow Chevron down icon Chevron up icon
Chapter 2: Variational Autoencoder Chevron down icon Chevron up icon
Chapter 3: Generative Adversarial Network Chevron down icon Chevron up icon
Section 2: Applications of Deep Generative Models Chevron down icon Chevron up icon
Chapter 4: Image-to-Image Translation Chevron down icon Chevron up icon
Chapter 5: Style Transfer Chevron down icon Chevron up icon
Chapter 6: AI Painter Chevron down icon Chevron up icon
Section 3: Advanced Deep Generative Techniques Chevron down icon Chevron up icon
Chapter 7: High Fidelity Face Generation Chevron down icon Chevron up icon
Chapter 8: Self-Attention for Image Generation Chevron down icon Chevron up icon
Chapter 9: Video Synthesis Chevron down icon Chevron up icon
Chapter 10: Road Ahead Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.7
(6 Ratings)
5 star 66.7%
4 star 33.3%
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syeduzzaman khan Mar 01, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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 Amazon
MLEngineer Feb 28, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.)
Amazon Verified review Amazon
BB Mar 08, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Clean explanation of the most important techniques! Even advanced topics like StyleGAN were explained very well!
Amazon Verified review Amazon
Susie Jun 25, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
Vince S. Feb 07, 2021
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
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.
Amazon Verified review Amazon
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