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

Foster, David

ISBN 10: 1492041947 / ISBN 13: 9781492041948

Verlag: O'Reilly Media, 2019

Beschreibung

Gently used with minimal wear on the corners and cover. A few pages may contain light highlighting or writing, but the text remains fully legible. Dust jacket may be missing, and supplemental materials like CDs or codes may not be included. May be ex-library with library markings. Ships promptly!

Bestandsnummer des Verkäufers DBV.1492041947.G

Bibliografische Details

• Titel: Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play
• Verlag: O'Reilly Media
• Veröffentlichungsdatum: 2019
• Sprache: Englisch
• ISBN-10: 1492041947
• ISBN-13: 9781492041948
• Bindung: Softcover
• Zustand: good

Über diesen Titel

Inhaltsangabe

Generative modeling is one of the hottest topics in AI. It's now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. 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.

Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative.

• Discover how variational autoencoders can change facial expressions in photos
• Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation
• Create recurrent generative models for text generation and learn how to improve the models using attention
• Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting
• Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN

Über die Autorin bzw. den Autor

David Foster is the co-founder of Applied Data Science, a data science consultancy delivering bespoke solutions for clients. He holds an MA in Mathematics from Trinity College, Cambridge, UK and an MSc in Operational Research from the University of Warwick. David has won several international machine learning competitions, including the Innocentive Predicting Product Purchase challenge and was awarded first prize for a visualisation that enables a pharmaceutical company in the US to optimize site selection for clinical trials. He is an active participant in the online data science community and has authored several successful blog posts on deep reinforcement learning including `How To Build Your Own AlphaZero AI'.