Deep learning / (Record no. 15266)

MARC details
000 -LEADER
fixed length control field 02343 a2200229 4500
001 - CONTROL NUMBER
control field TB12757
003 - CONTROL NUMBER IDENTIFIER
control field IN-BhIIT
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20260313175229.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 260205b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9780262035613 (hbk.)
040 ## - CATALOGING SOURCE
Original cataloging agency IN-BhIIT
041 ## - LANGUAGE CODE
Language code of text eng
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.3
Book number GOO/D
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Goodfellow, Ian
Relator term Author
245 ## - TITLE STATEMENT
Title Deep learning /
Statement of responsibility, etc Ian Goodfellow, Yoshua Bengio, Aaron Courville
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication Londaon :
Name of publisher MIT Press,
Year of publication 2016.
300 ## - PHYSICAL DESCRIPTION
Number of Pages xxii, 775 p. :
Other physical details(ill.) ill. ;
Dimensions(size) 22 cm.
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
Relator term Joint Author
Personal name Courville, Aaron
Relator term Joint Author
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Text Book
Koha issues (borrowed), all copies 1
Holdings
Withdrawn status Lost status Damaged status Not for loan Collection code Home library Current library Date acquired Source of acquisition Cost, normal purchase price Full call number Accession Number Cost, replacement price Price effective from Koha item type
Not withdrawn Not Lost not damaged     Central Library, IIT Bhubaneswar Central Library, IIT Bhubaneswar 21/01/2026 39 6557.59 006.3 GOO/D TB12757 8983.00 21/01/2026 Text Book
Not withdrawn Not Lost not damaged     Central Library, IIT Bhubaneswar Central Library, IIT Bhubaneswar 21/01/2026 39 6557.59 006.3 GOO/D TB12756 8983.00 21/01/2026 Course Reserve

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