Deep Learning

Ian Goodfellow

ISBN 10: 0262035618 / ISBN 13: 9780262035613

Published by The MIT Press Nov 2016, 2016

About this Item

Neuware -An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.Libri GmbH, Europaallee 1, 36244 Bad Hersfeld 775 pp. Englisch.

Seller Inventory # 9780262035613

Bibliographic Details

• Title: Deep Learning
• Publisher: The MIT Press Nov 2016
• Publication Date: 2016
• Language: English
• ISBN 10: 0262035618
• ISBN 13: 9780262035613
• Binding: Buch
• Condition: Neu
• Dimensions: 234W x 185H x 33D millimeters 234 millimeters width by 185 millimeters height by 33 millimeters depth
• Weight: 1330 grams

About this title

Synopsis

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.

“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.”
—Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.

The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.

Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

About the Author

Ian Goodfellow is a Research Scientist at Google.

Yoshua Bengio is Professor of Computer Science at the Université de Montréal.

Aaron Courville is Assistant Professor of Computer Science at the Université de Montréal.