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Deep learning in time series analysis /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Deep learning in time series analysis // Arash Gharehbaghi.
作者:	Gharehbaghi, Arash,
出版者:	Boca Raton, FL :CRC Press, : c2023.,
面頁冊數:	xi, 195 p. :ill. (some col.) ; : 25 cm.;
標題:	Deep learning (Machine learning) -
ISBN:	9780367321789 (hbk.) :

Deep learning in time series analysis /
Gharehbaghi, Arash,1972-

Deep learning in time series analysis /Arash Gharehbaghi. - 1st ed. - Boca Raton, FL :CRC Press,c2023. - xi, 195 p. :ill. (some col.) ;25 cm.

Includes bibliographical references and index.

"The concept of deep machine learning becomes easier to understandable by paying attention to the cyclic stochastic time series and a time series whose content is non-stationary not only within the cycles, but also over the cycles as the beat to beat variations. This book introduces original deep learning methods for classification of such the time series using proposed clustering methods as the learning tools at the deep level"--

ISBN: 9780367321789 (hbk.) :NT5023Subjects--Topical Terms:

1381171
Deep learning (Machine learning)

LC Class. No.: QA280

Dewey Class. No.: 006.31

Deep learning in time series analysis /
LDR:01176cam a2200229 a 4500 001 1131028
008 241204s2023 flua b 001 0 eng d
020 $a 9780367321789 (hbk.) : $c NT5023
020 $a 0367321785 (hbk.)
020 $a 1032418869 (pbk.)
020 $a 9781032418865 (pbk.)
020 $z 0429321252 (ebk.)
020 $z 9780429321252 (ebk.)
035 $a (OCoLC)1378643620 $z (OCoLC)1390800824
040 $a TL $b eng $c TL $d NFU $e AACR2
050 # 4 $a QA280
082 0 4 $a 006.31
100 1 $a Gharehbaghi, Arash, $d 1972- $3 1451834
245 1 0 $a Deep learning in time series analysis / $c Arash Gharehbaghi.
250 $a 1st ed.
260 # $a Boca Raton, FL : $b CRC Press, $c c2023.
300 $a xi, 195 p. : $b ill. (some col.) ; $c 25 cm.
504 $a Includes bibliographical references and index.
520 # $a "The concept of deep machine learning becomes easier to understandable by paying attention to the cyclic stochastic time series and a time series whose content is non-stationary not only within the cycles, but also over the cycles as the beat to beat variations. This book introduces original deep learning methods for classification of such the time series using proposed clustering methods as the learning tools at the deep level"-- $c Provided by publisher.
650 # 0 $a Deep learning (Machine learning) $3 1381171
650 # 0 $a Time-series analysis $x Data processing. $3 671213

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