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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

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Product type Paperback
Published in Apr 2021
Publisher Packt
ISBN-13 9781800200883
Length 488 pages
Edition 1st Edition
Languages
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Authors (2):
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Raghav Bali Raghav Bali
Author Profile Icon Raghav Bali
Raghav Bali
Joseph Babcock Joseph Babcock
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Joseph Babcock
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Table of Contents (16) Chapters Close

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

The Rise of Methods for Text Generation

In the preceding chapters, we discussed different methods and techniques to develop and train generative models. Particularly, in Chapter 6, Image Generation with GANs, we discussed the taxonomy of generative models and introduced explicit and implicit classes. Throughout this book, our focus has been on developing generative models in the vision space, utilizing image and video datasets. The advancements in the field of deep learning for computer vision and ease of understanding were the major reasons behind such a focused introduction.

In the past couple of years though, Natural Language Processing (NLP) or processing of textual data has seen great interest and research. Text is not just another unstructured type of data; there's a lot more to it than what meets the eye. Textual data is a representation of our thoughts, ideas, knowledge, and communication.

In this chapter and the next, we will focus on understanding concepts...

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