Transfer learning / (Record no. 54659)

MARC details
000 -LEADER
fixed length control field 02131nam a2200325 i 4500
003 - CONTROL NUMBER IDENTIFIER
control field IN-BdCUP
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250103125021.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 110418s2020||||enk o ||1 0|eng|d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781139061773 (ebook)
Canceled/invalid ISBN 9781107016903 (hardback)
040 ## - CATALOGING SOURCE
Original cataloging agency IN-BdCUP
Language of cataloging eng
Transcribing agency IN-BdCUP
Description conventions rda
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
050 ## - LIBRARY OF CONGRESS CALL NUMBER
Classification number Q325.5
Item number .Y366 2020
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.3/1
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Yang, Qiang
Relator term Author
245 #0 - TITLE STATEMENT
Title Transfer learning /
Statement of responsibility, etc. Qiang Yang, Yu Zhang, Wenyuan Dai, Sinno Jialin Pan.
264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture Cambridge :
Name of producer, publisher, distributor, manufacturer Cambridge University Press,
Date of production, publication, distribution, manufacture, or copyright notice 2020
300 ## - PHYSICAL DESCRIPTION
Extent 1 online resource (xi, 379 pages) :
Other physical details digital, PDF file(s).
336 ## - CONTENT TYPE
Content type term text
Content type code txt
337 ## - MEDIA TYPE
Source rdamedia
338 ## - CARRIER TYPE
Carrier type term online resource
Carrier type code cr
Source rdacarrier
500 ## - GENERAL NOTE
General note Title from publisher's bibliographic system (viewed on 29 Jan 2020).
520 ## - SUMMARY, ETC.
Summary, etc. Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small amount of data available. This makes such systems more reliable and robust, keeping the machine learning model faced with unforeseeable changes from deviating too much from expected performance. At an enterprise level, transfer learning allows knowledge to be reused so experience gained once can be repeatedly applied to the real world. For example, a pre-trained model that takes account of user privacy can be downloaded and adapted at the edge of a computer network. This self-contained, comprehensive reference text describes the standard algorithms and demonstrates how these are used in different transfer learning paradigms. It offers a solid grounding for newcomers as well as new insights for seasoned researchers and developers.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine learning.
-- Artificial intelligence.
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Dai, Wenyuan,
Relator term author.
Personal name Pan, Sinno Jialin,
Relator term author.
Personal name Zhang, Yu
Relator term author.
776 ## - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Print version:
International Standard Book Number 9781139061773
856 ## - ELECTRONIC LOCATION AND ACCESS
Materials specified Electronic Book Resource
Uniform Resource Identifier <a href="https://doi.org/10.1017/9781139061773">https://doi.org/10.1017/9781139061773</a>
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type E-Book
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 Order Number Cost, normal purchase price Bill number Total checkouts Full call number Barcode Date last seen Uniform resource identifier Actual Cost, replacement price Bill Date Koha item type
    Dewey Decimal Classification     Ranganathan Library Ranganathan Library 01/01/2025 Today & Tomorrow's Printers and Publishers CUP/LIB/24 270.00 TTPP/202/2024-25   6.31 E01850 03/01/2025 https://doi.org/10.1017/9781139061773 270.00 13/11/2024 E-Book
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