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Princeton University Library Catalog
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Deep learning / Ian Goodfellow, Yoshua Bengio and Aaron Courville.
Author
Goodfellow, Ian
[Browse]
Format
Book
Language
English
Published/Created
Cambridge, Massachusetts : The MIT Press, [2016]
©2016
Description
xxii, 775 pages : illustrations (some color) ; 24 cm.
Availability
Available Online
free eBooks
Copies in the Library
Location
Call Number
Status
Location Service
Notes
Engineering Library - Reserve
Q325.5 .G66 2016
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Details
Subject(s)
Machine learning
[Browse]
Author
Bengio, Yoshua
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Courville, Aaron
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Series
Adaptive computation and machine learning
[More in this series]
Summary note
"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 video games. 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"-- Page 4 of cover.
Bibliographic references
Includes bibliographical references and index.
Contents
Applied math and machine learning basics. Linear algebra
Probability and information theory
Numerical computation
Machine learning basics
Deep networks: modern practices. Deep feedforward networks
Regularization for deep learning
Optimization for training deep models
Convolutional networks
Sequence modeling: recurrent and recursive nets
Practical methodology
Applications
Deep learning research. Linear factor models
Autoencoders
Representation learning
Structured probabilistic models for deep learning
Monte Carlo methods
Confronting the partition function
Approximate inference
Deep generative models.
Show 16 more Contents items
ISBN
0262035618 ((hardcover ; : alkaline paper))
9780262035613 ((hardcover ; : alkaline paper))
LCCN
2016022992
OCLC
955778308
Statement on language in description
Princeton University Library aims to describe library materials in a manner that is respectful to the individuals and communities who create, use, and are represented in the collections we manage.
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Deep learning / Ian Goodfellow, Yoshua Bengio and Aaron Courville.
id
99125319049706421
Deep learning / Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
id
SCSB-14148720