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Generative AI with Python and TensorFlow 2

You're reading from Generative AI with Python and TensorFlow 2 Create images, text, and music with VAEs, GANs, LSTMs, Transformer models

Product type Paperback

Published in Apr 2021

Publisher Packt

ISBN-13 9781800200883

Length 488 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Artificial Intelligence

Authors (2):

Raghav Bali

Joseph Babcock

View More author details

Table of Contents (16) Chapters

Preface

1. An Introduction to Generative AI: "Drawing" Data from Models

2. Setting Up a TensorFlow Lab FREE CHAPTER

3. Building Blocks of Deep Neural Networks

4. Teaching Networks to Generate Digits

5. Painting Pictures with Neural Networks Using VAEs

6. Image Generation with GANs

7. Style Transfer with GANs

8. Deepfakes with GANs

9. The Rise of Methods for Text Generation

10. NLP 2.0: Using Transformers to Generate Text

11. Composing Music with Generative Models

12. Play Video Games with Generative AI: GAIL

13. Emerging Applications in Generative AI

14. Other Books You May Enjoy

15. Index

Modes of operation

Generating believable fake content requires taking care of multiple aspects to ensure that the results are as authentic as possible. A typical deepfake setup requires a source, a target, and the generated content.

The source, denoted with subscript s, is the driver identity that controls the required output
The target, denoted with subscript t, is the identity being faked
The generated content, denoted with subscript g, is the result following the transformation of the source to the target.

Now that we have some basic terminology in place, let's dive deeper and understand different ways of generating fake content.

Replacement

This is the most widely used form of generating fake content. The aim is to replace specific content of the target (x_t) with that from the source (x_s). Face replacement has been an active area of research for quite some time now. Figure 8.1 shows Donald Trump's face being replaced with Nicolas...

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Authors (2)

Joseph Babcock

Joseph Babcock

Joseph Babcock has spent over a decade working with big data and AI in the e-commerce, digital streaming, and quantitative finance domains. Throughout his career, he has worked on recommender systems, petabyte-scale cloud data pipelines, A/B testing, causal inference, and time series analysis. He completed his PhD studies at Johns Hopkins University, applying machine learning to drug discovery and genomics.

See other products by Joseph Babcock

Raghav Bali

Raghav Bali

Raghav Bali is a Staff Data Scientist at Delivery Hero, a leading food delivery service headquartered in Berlin, Germany. With 12+ years of expertise, he specializes in research and development of enterprise-level solutions leveraging Machine Learning, Deep Learning, Natural Language Processing, and Recommendation Engines for practical business applications. Besides his professional endeavors, Raghav is an esteemed mentor and an accomplished public speaker. He has contributed to multiple peer-reviewed papers and authored multiple well received books. Additionally, he holds co-inventor credits on multiple patents in healthcare, machine learning, deep learning, and natural language processing.

See other products by Raghav Bali

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