Apress | Generating A New Reality: From Autoencoders And Adversarial Networks To Deepfakes (2021 EN)

Author: Micheal Micheal Lanham
Full Title: Generating A New Reality: From Autoencoders And Adversarial Networks To Deepfakes
Publisher: ‎ Apress; 1st ed. edition (July 16, 2021)
Year: 2021
ISBN-13: 9781484270929 (978-1-4842-7092-9), 9781484270912 (978-1-4842-7091-2)
ISBN-10: 1484270924, 1484270916
Pages: 321
Language: English
Genre: Educational: Machine Learning
File type: EPUB (True), PDF (True)
Quality: 10/10
Price: 48.14 €


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.


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

Features:
✓ 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)

Who This Book Is For:
Machine learning developers and AI enthusiasts who want to understand AI content generation techniques.

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