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

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...