Book Image

Generative AI with Python and TensorFlow 2

By : Joseph Babcock, Raghav Bali
4 (1)
Book Image

Generative AI with Python and TensorFlow 2

4 (1)
By: Joseph Babcock, Raghav Bali

Overview of this book

Machines are excelling at creative human skills such as painting, writing, and composing music. Could you be more creative than generative AI? In this book, you’ll explore the evolution of generative models, from restricted Boltzmann machines and deep belief networks to VAEs and GANs. You’ll learn how to implement models yourself in TensorFlow and get to grips with the latest research on deep neural networks. There’s been an explosion in potential use cases for generative models. You’ll look at Open AI’s news generator, deepfakes, and training deep learning agents to navigate a simulated environment. Recreate the code that’s under the hood and uncover surprising links between text, image, and music generation.
Table of Contents (16 chapters)
14
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15
Index

Deepfakes overview

Deepfakes is an all-encompassing term representing content generated using artificial intelligence (in particular, deep learning) that seems realistic and authentic to a human being. The generation of fake content or manipulation of existing content to suit the needs and agenda of the entities involved is not new. In the introduction, we discussed a few movies where CGI and painstaking manual effort helped in generating realistic results. With advancements in deep learning and, more specifically, generative models, it is becoming increasingly difficult to differentiate between what is real and what is fake.

Generative Adversarial Networks (GANs) have played a very important role in this space by enabling the generation of sharp, high-quality images and videos. Works such as https://thispersondoesnotexist.com, based on StyleGAN, have really pushed the boundaries in terms of the generation of high-quality realistic content. A number of other key architectures...