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Generative AI with Python and TensorFlow 2

You're reading from  Generative AI with Python and TensorFlow 2

Product type Book
Published in Apr 2021
Publisher Packt
ISBN-13 9781800200883
Pages 488 pages
Edition 1st Edition
Languages
Authors (2):
Joseph Babcock Joseph Babcock
Profile icon Joseph Babcock
Raghav Bali Raghav Bali
Profile icon Raghav Bali
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 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

Emerging Applications in Generative AI

In the preceding chapters, we have examined a large number of applications using generative AI, from generating pictures and text to even music. However, this is a large and ever-expanding field; the number of publications on Google Scholar matching a search for "generative adversarial networks" is 27,200, of which 16,200 were published in 2020! This is astonishing for a field that essentially started in 2014, the exponential growth of which can also be appreciated on the Google n-gram viewer (Figure 13.1):

Figure 13.1: Google n-gram of "generative adversarial networks"

As we saw in this volume, generative adversarial networks are only one class of models in the broader field of generative AI, which also includes models such as variational autoencoders, BERT, and GPT-3. As a single book cannot hope to cover all of these areas, we conclude this volume with discussion of a number of emerging topics in this field...

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