Deep Learning (Record no. 59168)

MARC details
000 -LEADER
fixed length control field 03388nam a22003137a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220921160342.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220505b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780262035613
028 ## - DISTRIBUTOR NAME
Distributor Name :International Book Distributors
Distributor address :Flat No 17, Prakash Apartment 4405/2, 5 Ansari Road Darya Ganj New Delhi
Bill Number :117716
Bill Date :22/04/2022
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
Statement of responsibility, etc / Ian Goodfellow
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc Cambridge, Massachusetts
Name of publisher, distributor, etc : The MIT Press
Date of publication, distribution, etc ©2016
300 ## - PHYSICAL DESCRIPTION
Extent xxii, 775p.
Other physical details : illustrations some color
Dimensions ; 24 cm
365 ## - TRADE PRICE
Price amount 6328
490 ## - SERIES STATEMENT
Series statement Adaptive computation and machine learning
505 ## - FORMATTED CONTENTS NOTE
Contents note Introduction --<br/>Applied math and machine learning basics. Linear algebra --<br/>Probability and information theory --<br/>Numerical computation --<br/>Machine learning basics --<br/>Deep networks: modern practices. Deep feedforward networks --<br/>Regularization for deep learning --<br/>Optimization for training deep models --<br/>Convolutional networks --<br/>Sequence modeling: recurrent and recursive nets --<br/>Practical methodology --<br/>Applications --<br/>Deep learning research. Linear factor models --<br/>Autoencoders --<br/>Representation learning --<br/>Structured probabilistic models for deep learning --<br/>Monte Carlo methods --<br/>Confronting the partition function --<br/>Approximate inference --<br/>Deep generative models.
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. 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
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer Science
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Special computer methods
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 Apprentissage automatique.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computers and IT.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Maschinelles Lernen
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Goodfellow, Ian
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 Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Total Renewals Full call number Barcode Checked out Date last seen Date checked out Price effective from Koha item type
    Dewey Decimal Classification     SNU LIBRARY SNU LIBRARY 05/05/2022 International Book Distributors 6328.00 6 10 006.31 GOO 28677 31/12/2025 05/12/2023 05/12/2023 05/05/2022 Books
© Copyright Shiv Nadar University 2012. All Rights Reserved.  Disclaimer |  Sitemap
The Shiv Nadar University has been established under U.P. Act No 12 of 2011. Shiv Nadar University is UGC Approved.