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Generating a New Reality

From Autoencoders and Adversarial Networks to Deepfakes

  • Book
  • © 2021

Overview

Authors:
  1. Micheal Lanham

    1. Calgary, Canada

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  • Explores variations of content generation AI, not just GANs
  • Uses free online resources (such as Google Collaboratory) that allow users to train AI with GPUs on the cloud
  • Is developer-focused, with lots of hands-on exercises (readers are encouraged to open the examples and run them while reading through the book)

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-xvii
    Download chapter PDF

  2. The Basics of Deep Learning

    • Micheal Lanham
    Pages 1-34

  3. Unleashing Generative Modeling

    • Micheal Lanham
    Pages 35-68

  4. Exploring the Latent Space

    • Micheal Lanham
    Pages 69-103

  5. GANs, GANs, and More GANs

    • Micheal Lanham
    Pages 105-133

  6. Image to Image Content Generation

    • Micheal Lanham
    Pages 135-165

  7. Residual Network GANs

    • Micheal Lanham
    Pages 167-193

  8. Attention Is All We Need!

    • Micheal Lanham
    Pages 195-222

  9. Advanced Generators

    • Micheal Lanham
    Pages 223-253

  10. Deepfakes and Face Swapping

    • Micheal Lanham
    Pages 255-285

  11. Cracking Deepfakes

    • Micheal Lanham
    Pages 287-302

  12. Back Matter

    Pages 303-321
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Keywords

About this book

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





Authors and Affiliations

  • Calgary, Canada

    Micheal Lanham

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.

Bibliographic Information

  • Book Title: Generating a New Reality

  • Book Subtitle: From Autoencoders and Adversarial Networks to Deepfakes

  • Authors: Micheal Lanham

  • DOI: https://doi.org/10.1007/978-1-4842-7092-9

  • Publisher: Apress Berkeley, CA

  • eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)

  • Copyright Information: Micheal Lanham 2021

  • Softcover ISBN: 978-1-4842-7091-2Published: 16 July 2021

  • eBook ISBN: 978-1-4842-7092-9Published: 15 July 2021

  • Edition Number: 1

  • Number of Pages: XVII, 321

  • Number of Illustrations: 120 b/w illustrations

  • Topics: Machine Learning, Python

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