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

Why use generative models?

Now that we have reviewed what generative models are and defined them more formally in the language of probability, why would we have a need for such models in the first place? What value do they provide in practical applications? To answer this question, let's take a brief tour of the topics that we will cover in more detail in the rest of this book.

The promise of deep learning

As noted already, many of the models we will survey in the book are deep, multi-level neural networks. The last 15 years have seen a renaissance in the development of deep learning models for image classification, natural language processing and understanding, and reinforcement learning. These advances were enabled by breakthroughs in traditional challenges in tuning and optimizing very complex models, combined with access to larger datasets, distributed computational power in the cloud, and frameworks such as TensorFlow that make it easier to prototype and...