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Generating a New Reality: From Autoencoders and Adversarial Networks to Deepfakes de Micheal Lanham

Generating a New Reality: From Autoencoders and Adversarial Networks to Deepfakes

Autor Micheal Lanham
en Limba Engleză Paperback – 16 iul 2021
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

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Specificații

ISBN-13: 9781484270912
ISBN-10: 1484270916
Pagini: 300
Ilustrații: XVII, 321 p. 120 illus.
Dimensiuni: 178 x 254 x 24 mm
Greutate: 0.59 kg
Ediția:1st ed.
Editura: Apress
Colecția Apress
Locul publicării:Berkeley, CA, United States

Cuprins

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 Generation with GANs.- Chapter 6: Residual Network GANs.- Chapter 7: Attention Is All We Need.- Chapter 8: Advanced Generators.- Chapter 9: Deepfakes and Faceswapping.- Chapter 10: Cracking Deepfakes.- Appendix A: Running Google Colab Locally.- Appendix B: Opening a Notebook.- Appendix C: Connecting Google Drive and Saving.

Notă biografică

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.

Textul de pe ultima copertă

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.

Youwill:
  • 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



Caracteristici

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)