Generative deep learning : Teaching machines to paint, write, compose, and play DAVID FOSTER

By:

Foster, David

Contributor(s):

Demarest, Rebecca

Material type: Text

TextPublication details: Mumbai: O'Reilly: Shroff Publishers and Distributors, c.2019.Edition: 1 EdDescription: xv+308pISBN:

978-93-5213-871-5

Subject(s):

Machine learning. Artificial intelligence. Neural networks (Computer science) Generative programming (Computer science)

DDC classification:

006.31 FOS-G

Contents:

Part 1. Introduction to generative deep learning. Generative modeling -- Deep learning -- Variational autoencoders -- Generative adversarial networks -- Part 2. Teaching machines to paint, write, compose, and play. Paint -- Write -- Compose -- Play -- The future of generative modeling -- Conclusion.

Summary: With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models.

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Holdings
Item type	Current library	Home library	Call number	Status	Date due	Barcode
Book	Dept. of Computational Biology and Bioinformatics Processing Center	Dept. of Computational Biology and Bioinformatics	006.31 FOS-G (Browse shelf(Opens below))	Available		DCB3857

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Previous	006.31 DUT-M Machine learning	006.31 ETH-I Introduction To Machine Learning	006.31 FEN-M Machine learning with Python for everyone	006.31 FOS-G Generative deep learning : Teaching machines to paint, write, compose, and play	006.31 GOO-D Deep Learning	006.31 GOP-A Applied machine learning	006.31 HAS.E The elements of statistical learning: Data Mining, Inference, and Prediction	Next

Part 1. Introduction to generative deep learning. Generative modeling --
Deep learning --
Variational autoencoders --
Generative adversarial networks --
Part 2. Teaching machines to paint, write, compose, and play. Paint --
Write --
Compose --
Play --
The future of generative modeling --
Conclusion.

With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models.

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