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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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Toc

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

Getting started with music generation

Music generation is an inherently complex and difficult task. Doing so with the help of algorithms (machine learning or otherwise) is even more challenging. Nevertheless, music generation is an interesting area of research with a number of open problems and fascinating works.

In this section, we will build a high-level understanding of this domain and understand a few important and foundational concepts.

Computer-assisted music generation or, more specifically, deep music generation (due to the use of deep learning architectures) is a multi-level learning task composed of score generation and performance generation as its two major components. Let's briefly discuss each of these components:

  • Score generation: A score is a symbolic representation of music that can be used/read by humans or systems to produce music. To draw an analogy, we can safely consider the relationship between scores and music to be similar to...
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