Deep Learning (Record no. 51047)

MARC details
000 -LEADER
fixed length control field 02143 a2200193 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250809110219.0
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780262035613
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31 GOO
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Goodfellow, Ian
245 ## - TITLE STATEMENT
Title Deep Learning
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc MIT Press
Place of publication, distribution, etc London
Date of publication, distribution, etc 2016
300 ## - PHYSICAL DESCRIPTION
Extent 775
520 ## - SUMMARY, ETC.
Summary, etc 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.<br/><br/>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.<br/><br/>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
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine Learning
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computation and Machine Learning
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Bengio, Yoshua
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Courville, Aaron
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Book
Source of classification or shelving scheme Dewey Decimal Classification

No items available.