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Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play. David Foster

Generative-Deep-Learning-Teaching.pdf
ISBN: 9781492041948 | 322 pages | 9 Mb

• Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play
• David Foster
• Page: 322
• Format: pdf, ePub, fb2, mobi
• ISBN: 9781492041948
• Publisher: O'Reilly Media, Incorporated

Download Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play

Kindle ipod touch download ebooks Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play by David Foster

Generative modeling is one of the hottest topics in artificial intelligence. Recent advances in the field have shown how it’s possible to teach a machine to excel at human endeavors—such as drawing, composing music, and completing tasks—by generating an understanding of how its actions affect its environment. With this practical book, machine learning engineers and data scientists will learn how to recreate some of the most famous examples of generative deep learning models, such as variational autoencoders and generative adversarial networks (GANs). You’ll also learn how to apply the techniques to your own datasets. David Foster, cofounder of Applied Data Science, demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to the most cutting-edge algorithms in the field. Through tips and tricks, you’ll learn how to make your models learn more efficiently and become more creative. Get a fundamental overview of generative modeling Learn how to use the Keras and TensorFlow libraries for deep learning Discover how variational autoencoders (VAEs) work Get practical examples of generative adversarial networks (GANs) Understand how to build generative models that learn how to paint, write, and compose Apply generative models within a reinforcement learning setting to accomplish tasks

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A research project exploring the role of machine learning in the process of creating art and music. arXiv:1903.04411v2 [cs.CV] 22 Mar 2019
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