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Hands-On Music Generation with Magenta

You're reading from   Hands-On Music Generation with Magenta Explore the role of deep learning in music generation and assisted music composition

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Product type Paperback
Published in Jan 2020
Publisher
ISBN-13 9781838824419
Length 360 pages
Edition 1st Edition
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Author (1):
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Alexandre DuBreuil Alexandre DuBreuil
Author Profile Icon Alexandre DuBreuil
Alexandre DuBreuil
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Table of Contents (16) Chapters Close

Preface 1. Section 1: Introduction to Artwork Generation
2. Introduction to Magenta and Generative Art FREE CHAPTER 3. Section 2: Music Generation with Machine Learning
4. Generating Drum Sequences with the Drums RNN 5. Generating Polyphonic Melodies 6. Latent Space Interpolation with MusicVAE 7. Audio Generation with NSynth and GANSynth 8. Section 3: Training, Learning, and Generating a Specific Style
9. Data Preparation for Training 10. Training Magenta Models 11. Section 4: Making Your Models Interact with Other Applications
12. Magenta in the Browser with Magenta.js 13. Making Magenta Interact with Music Applications 14. Assessments 15. Other Books You May Enjoy

Using Google Cloud Platform

Using a cloud computing provider is useful to offload computing to faster machines. It can also be used if we want to make multiple runs at the same time. For example, we could try fixing exploding gradients by launching two runs: one with a lower learning rate and one with a lower gradient clipping. We could spawn two different VMs, each training its own model, and see which performs better.

We are going to use Google Cloud Platform (GCP), but other cloud providers, such as Amazon AWS or Microsoft Azure, will also work. We'll go through the different steps needed to train a Melody RNN model on the piano jazz dataset from the previous chapter, including the GCP account configuration and VM instance creation.

Creating and configuring an account

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