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Generating a New Reality

From Autoencoders and Adversarial Networks to Deepfakes
BuchKartoniert, Paperback
321 Seiten
Englisch
Springererschienen am16.07.20211st ed.
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 LearnKnow the fundamentals of content generation from autoencoders to generative adversarial networks (GANs)Explore variations of GANUnderstand the basics of other forms of content generationUse advanced projects such as Faceswap, deepfakes, DeOldify, and StyleGAN2Who This Book Is ForMachine learning developers and AI enthusiasts who want to understand AI content generation techniquesmehr
Verfügbare Formate
BuchKartoniert, Paperback
EUR69,54
E-BookPDF1 - PDF WatermarkE-Book
EUR66,99

Produkt

KlappentextThe 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 LearnKnow the fundamentals of content generation from autoencoders to generative adversarial networks (GANs)Explore variations of GANUnderstand the basics of other forms of content generationUse advanced projects such as Faceswap, deepfakes, DeOldify, and StyleGAN2Who This Book Is ForMachine learning developers and AI enthusiasts who want to understand AI content generation techniques
Details
ISBN/GTIN978-1-4842-7091-2
ProduktartBuch
EinbandartKartoniert, Paperback
Verlag
Erscheinungsjahr2021
Erscheinungsdatum16.07.2021
Auflage1st ed.
Seiten321 Seiten
SpracheEnglisch
IllustrationenXVII, 321 p. 120 illus.
Artikel-Nr.16382388

Inhalt/Kritik

Inhaltsverzeichnis
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.mehr

Autor

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.