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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

Concepts

Artificial Intelligence

Authors (2):

Joseph Babcock

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

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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Authors (2)

Joseph Babcock

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Raghav Bali

See other products by Raghav Bali

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