Understand the theory behind deep generative models and experiment with practical examples

Key Features

• Build a solid understanding of the inner workings of generative models
• Experiment with practical TensorFlow 2.x implementations of state-of-the-art models
• Explore a wide range of current and emerging use cases for deep generative AI

Book Description

Deep generative models are powerful tools that rival human creative capabilities. In this book, you'll discover how these models emerged, from restricted Boltzmann machines and deep belief networks to VAEs, GANs, and beyond. You'll develop a foundational understanding of generative AI and learn how to implement models yourself in TensorFlow, supported by references to seminal and current research.

After getting to grips with the fundamentals of deep neural networks, you'll set up a scalable code lab in the cloud and begin to explore the huge breadth of potential use cases for generative models. You'll look at Open AI's news generator, networks for style transfer and deepfakes, synergy with reinforcement learning, and more. As you progress, you'll recreate the code that makes these possible, piecing together TensorFlow layers, utility functions, and training loops to uncover links between the different modes of generation.

By the end of this book, you will have acquired the knowledge to create and implement your own generative AI models.

What you will learn

• Implement paired and unpaired style transfer with networks like StyleGAN
• Use facial landmarks, autoencoders, and pix2pix GAN to create deepfakes
• Build several text generation pipelines based on LSTMs, BERT, and GPT-2, learning how attention and transformers changed the NLP landscape
• Compose music using hands-on LSTM models, simple GANs, and the intricate MuseGAN
• Train a deep learning agent to move through a simulated physical environment
• Discover emerging applications of generative AI, such as folding proteins and creating videos from images

Who this book is for

This book will appeal to Python programmers, seasoned modelers, and machine learning engineers who are keen to learn about the creation and implementation of generative models. To make the most out of this book, you should have a basic familiarity with probability theory, linear algebra, and deep learning.