Deep Learning

Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron

ISBN 10: 0262035618 ISBN 13: 9780262035613
Published by MIT Press, 2016
Used Hardcover

From ThriftBooks-Reno, Reno, NV, U.S.A. Seller rating 5 out of 5 stars 5-star rating, Learn more about seller ratings

AbeBooks Seller since 25 May 2012

This specific item is no longer available.

About this Item

Description:

May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less. Seller Inventory # G0262035618I4N00

Report this item

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 Authors: Ian Goodfellow is Research Scientist at OpenAI.

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.

"About this title" may belong to another edition of this title.

Bibliographic Details

Title: Deep Learning
Publisher: MIT Press
Publication Date: 2016
Binding: Hardcover
Condition: Very Good
Dust Jacket Condition: No Jacket

Top Search Results from the AbeBooks Marketplace

There are 68 more copies of this book

View all search results for this book