Neural networks and deep learning: a textbook (Record no. 1566)

MARC details
000 -LEADER
fixed length control field 02137nam a22002177a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220204104335.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220204b ||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783319944623
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.32
Item number AGG
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Aggarwal, Charu C.
245 ## - TITLE STATEMENT
Title Neural networks and deep learning: a textbook
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc. Springer
Place of publication, distribution, etc. Switzerland
Date of publication, distribution, etc. 2018
300 ## - PHYSICAL DESCRIPTION
Extent xxi, 497 p.
365 ## - TRADE PRICE
Price type code EURO
Price amount 59.99
520 ## - SUMMARY, ETC.
Summary, etc. Introduction<br/>This book covers both classical and modern models in deep learning. The chapters of this book span three categories:<br/><br/>The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec.<br/><br/>Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines.<br/><br/>Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10.<br/><br/>The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Neural networks (Computer science)
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 Artificial intelligence
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer science
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Book
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Bill No Bill Date Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Full call number Accession Number Date last seen Date checked out Copy number Cost, replacement price Price effective from Koha item type
    Dewey Decimal Classification     IT & Decisions Sciences IN29920 28-01-2022 Indian Institute of Management LRC Indian Institute of Management LRC General Stacks 02/04/2022 Overseas Press India Private 3498.63 1 006.32 AGG 001691 11/06/2023 10/26/2023 1 5321.11 02/04/2022 Book

©2019-2020 Learning Resource Centre, Indian Institute of Management Bodhgaya

Powered by Koha