Deep learning (Record no. 32923)

000 -LEADER
fixed length control field 02233 a2200217 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20230822100914.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
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020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780262035613
040 ## - CATALOGING SOURCE
Original cataloging agency ICTS-TIFR
050 ## - LIBRARY OF CONGRESS CALL NUMBER
Classification number Q325.5 .G66
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Goodfellow, Ian
245 ## - TITLE STATEMENT
Title Deep learning
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Name of publisher, distributor, etc. The MIT Press,
Place of publication, distribution, etc. Cambridge, Massachusetts:
Date of publication, distribution, etc. c2016
300 ## - Physical Description
Pages: 775 p.
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.
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Bengio, Yoshua
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Courville, Aaron
856 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://www.deeplearningbook.org/">https://www.deeplearningbook.org/</a>
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Koha item type Book
Holdings
Withdrawn status Lost status Damaged status Not for loan Home library Shelving location Date acquired Inventory number Full call number Accession No. Koha item type
        ICTS Rack No 3 08/02/2023 IN246 dt.01st Aug 2023 Q325.5 .G66 02742 Book