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Probabilistic machine learning : = a...

Murphy, Kevin P., (1970-)

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Probabilistic machine learning : = an introduction /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Probabilistic machine learning :/ Kevin P. Murphy.
其他題名:	an introduction /
作者:	Murphy, Kevin P.,
出版者:	Cambridge, Mass. :The MIT Press, : c2022.,
面頁冊數:	xxix, 826 p. :ill. (some col.) ;24 cm.
標題:	Machine learning. -
ISBN:	9780262046824

Probabilistic machine learning : = an introduction /
Murphy, Kevin P.,1970-

Probabilistic machine learning :an introduction /Kevin P. Murphy. - Cambridge, Mass. :The MIT Press,c2022. - xxix, 826 p. :ill. (some col.) ;24 cm. - Adaptive computation and machine learning series. - Adaptive computation and machine learning..

Includes bibliographical references and index.

"This book provides a detailed and up-to-date coverage of machine learning. It is unique in that it unifies approaches based on deep learning with approaches based on probabilistic modeling and inference. It provides mathematical background (e.g. linear algebra, optimization), basic topics (e.g., linear and logistic regression, deep neural networks), as well as more advanced topics (e.g., Gaussian processes). It provides a perfect introduction for people who want to understand cutting edge work in top machine learning conferences such as NeurIPS, ICML and ICLR"--

ISBN: 9780262046824NT2650.00

LCCN: 2021027430Subjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / M87 2022

Dewey Class. No.: 006.31

Probabilistic machine learning : = an introduction /
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100 1 $a Murphy, Kevin P., $d 1970- $3 3692788
245 1 0 $a Probabilistic machine learning : $b an introduction / $c Kevin P. Murphy.
260 # $a Cambridge, Mass. : $b The MIT Press, $c c2022.
300 $a xxix, 826 p. : $b ill. (some col.) ; $c 24 cm.
490 1 $a Adaptive computation and machine learning series
504 $a Includes bibliographical references and index.
520 # $a "This book provides a detailed and up-to-date coverage of machine learning. It is unique in that it unifies approaches based on deep learning with approaches based on probabilistic modeling and inference. It provides mathematical background (e.g. linear algebra, optimization), basic topics (e.g., linear and logistic regression, deep neural networks), as well as more advanced topics (e.g., Gaussian processes). It provides a perfect introduction for people who want to understand cutting edge work in top machine learning conferences such as NeurIPS, ICML and ICLR"-- $c Provided by publisher.
650 # 0 $a Machine learning. $3 533906
650 # 0 $a Probabilities. $3 518889
650 # 2 $a Probability. $3 518898
830 0 $a Adaptive computation and machine learning. $3 1047988