國立虎尾科技大學 |

Distributed Machine Learning and Gradient Optimization

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Distributed Machine Learning and Gradient Optimization/ by Jiawei Jiang, Bin Cui, Ce Zhang.
作者:	Jiang, Jiawei.
其他作者:	Zhang, Ce.
面頁冊數:	XI, 169 p. 1 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Database Management. -
電子資源:	https://doi.org/10.1007/978-981-16-3420-8
ISBN:	9789811634208

Distributed Machine Learning and Gradient Optimization
Jiang, Jiawei.

Distributed Machine Learning and Gradient Optimization[electronic resource] /by Jiawei Jiang, Bin Cui, Ce Zhang. - 1st ed. 2022. - XI, 169 p. 1 illus.online resource. - Big Data Management,2522-0187. - Big Data Management,.

1 Introduction -- 2 Basics of Distributed Machine Learning -- 3 Distributed Gradient Optimization Algorithms -- 4 Distributed Machine Learning Systems -- 5 Conclusion. .

This book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the big data era, large-scale datasets pose enormous challenges for the existing machine learning systems. As such, implementing machine learning algorithms in a distributed environment has become a key technology, and recent research has shown gradient-based iterative optimization to be an effective solution. Focusing on methods that can speed up large-scale gradient optimization through both algorithm optimizations and careful system implementations, the book introduces three essential techniques in designing a gradient optimization algorithm to train a distributed machine learning model: parallel strategy, data compression and synchronization protocol. Written in a tutorial style, it covers a range of topics, from fundamental knowledge to a number of carefully designed algorithms and systems of distributed machine learning. It will appeal to a broad audience in the field of machine learning, artificial intelligence, big data and database management.

ISBN: 9789811634208

Standard No.: 10.1007/978-981-16-3420-8doiSubjects--Topical Terms:

669820
Database Management.

LC Class. No.: Q325.5-.7

Dewey Class. No.: 006.31

Distributed Machine Learning and Gradient Optimization
LDR:02654nam a22004095i 4500 001 1094848
003 DE-He213
005 20220711093014.0
007 cr nn 008mamaa
008 221228s2022 si | s |||| 0|eng d
020 $a 9789811634208 $9 978-981-16-3420-8
024 7 $a 10.1007/978-981-16-3420-8 $2 doi
035 $a 978-981-16-3420-8
050 4 $a Q325.5-.7
072 7 $a UYQM $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 006.31 $2 23
100 1 $a Jiang, Jiawei. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1403041
245 1 0 $a Distributed Machine Learning and Gradient Optimization $h [electronic resource] / $c by Jiawei Jiang, Bin Cui, Ce Zhang.
250 $a 1st ed. 2022.
264 1 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2022.
300 $a XI, 169 p. 1 illus. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Big Data Management, $x 2522-0187
505 0 $a 1 Introduction -- 2 Basics of Distributed Machine Learning -- 3 Distributed Gradient Optimization Algorithms -- 4 Distributed Machine Learning Systems -- 5 Conclusion. .
520 $a This book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the big data era, large-scale datasets pose enormous challenges for the existing machine learning systems. As such, implementing machine learning algorithms in a distributed environment has become a key technology, and recent research has shown gradient-based iterative optimization to be an effective solution. Focusing on methods that can speed up large-scale gradient optimization through both algorithm optimizations and careful system implementations, the book introduces three essential techniques in designing a gradient optimization algorithm to train a distributed machine learning model: parallel strategy, data compression and synchronization protocol. Written in a tutorial style, it covers a range of topics, from fundamental knowledge to a number of carefully designed algorithms and systems of distributed machine learning. It will appeal to a broad audience in the field of machine learning, artificial intelligence, big data and database management.
650 2 4 $a Database Management. $3 669820
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
650 1 4 $a Machine Learning. $3 1137723
650 0 $a Database management. $3 557799
650 0 $a Data mining. $3 528622
650 0 $a Machine learning. $3 561253
700 1 $a Zhang, Ce. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1317255
700 1 $a Cui, Bin. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1108804
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9789811634192
776 0 8 $i Printed edition: $z 9789811634215
776 0 8 $i Printed edition: $z 9789811634222
830 0 $a Big Data Management, $x 2522-0179 $3 1313442
856 4 0 $u https://doi.org/10.1007/978-981-16-3420-8
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)