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Personalized machine learning

[NT 42944] Record Type:	[NT 1579] Language materials, printed : [NT 40817] monographic
[NT 47261] Author:	McAuleyJulian,
[NT 47351] Place of Publication:	Cambridge, United Kingdom
[NT 47263] Published:	Cambridge University Press;
[NT 47352] Year of Publication:	2022
[NT 47264] Description:	x, 326 p. ill. : 24 cm.;
[NT 47266] Subject:	Machine learning. -
[NT 51398] Summary:	"Machine learning encompasses a broad range of problems ranging from detecting objects in images, finding documents relevant to a given query, or predicting the next element in a sequence, among countless others. Traditional approaches to these problems operate by collecting large, labeled datasets for training, uncovering informative features, and mining complex patterns that explain the association between features and labels. Typically, labels are regarded as an underlying 'truth' that should be predicted as accurately as possible"--Provided by publisher.
[NT 50961] ISBN:	978-1-316-51890-8bound

Personalized machine learning
McAuley, Julian

Personalized machine learning / Julian McAuley - Cambridge, United Kingdom : Cambridge University Press, 2022. - x, 326 p. ; ill. ; 24 cm..
Includes bibliographical references (p.306-321) and index..
ISBN 978-1-316-51890-8
Machine learning.

Personalized machine learning
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010 1 $a 978-1-316-51890-8 $b bound $d NT$1288
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102 $a gb
105 $a y a 001yy
200 1 $a Personalized machine learning $f Julian McAuley
210 $a Cambridge, United Kingdom $d 2022 $c Cambridge University Press
215 1 $a x, 326 p. $c ill. $d 24 cm.
320 $a Includes bibliographical references (p.306-321) and index.
330 $a "Machine learning encompasses a broad range of problems ranging from detecting objects in images, finding documents relevant to a given query, or predicting the next element in a sequence, among countless others. Traditional approaches to these problems operate by collecting large, labeled datasets for training, uncovering informative features, and mining complex patterns that explain the association between features and labels. Typically, labels are regarded as an underlying 'truth' that should be predicted as accurately as possible"--Provided by publisher.
606 # $a Machine learning. $3 126290
676 $a 006.31
700 1 $a McAuley $b Julian $3 419280
801 0 $a tw $b 嶺東科技大學圖書館

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