The emergence of artificial intelligence (AI) has brought us to the precipice of a new age where we struggle to understand what is real, from advanced CGI in movies to even faking the news. AI that was developed to understand our reality is now being used to create its own reality. 

In this book we look at the many AI techniques capable of generating new realities. We start with the basics of deep learning. Then we move on to autoencoders and generative adversarial networks (GANs). We explore variations of GAN to generate content. The book ends with an in-depth look at the most popular generator projects.

By the end of this book you will understand the AI techniques used to generate different forms of content. You will be able to use these techniques for your own amusement or professional career to both impress and educate others around you and give you the ability to transform your own reality into something new.

What You Will Learn

• Know the fundamentals of content generation from autoencoders to generative adversarial networks (GANs)
• Explore variations of GAN
• Understand the basics of other forms of content generation
• Use advanced projects such as Faceswap, deepfakes, DeOldify, and StyleGAN2

Who This Book Is For

Machine learning developers and AI enthusiasts who want to understand AI content generation techniques

Table of Contents

Chapter 1: The Basics of Deep Learning

Chapter 2: Unleashing Generative Modeling

Chapter 3: Exploring the Latent Space

Chapter 4: GANs, GANs, and More GANs

Chapter 5: Image to Image Content Generation

Chapter 6: Residual Network GANs

Chapter 7: Attention Is All We Need!

Chapter 8: Advanced Generators

Chapter 9: Deepfakes and Face Swapping

Chapter 10: Cracking Deepfakes

Appendix A: Running Google Colab Locally

Appendix B: Opening a Notebook

Appendix C: Connecting Google Drive and Saving

About the Author

Micheal Lanham is a proven software and tech innovator with more than 20 years of experience. During that time, he has developed a broad range of software applications in areas including games, graphics, web, desktop, engineering, artificial intelligence (AI), GIS, and machine learning (ML) applications for a variety of industries as an R&D developer. At the turn of the millennium, Micheal began working with neural networks and evolutionary algorithms in game development. He is an avid educator, has written more than eight books covering game development, extended reality, and AI, and teaches at meetups and other events. Micheal also likes to cook for his large family in his hometown of Calgary, Canada.