INTERNATIONAL STANDARD BOOK NUMBER
Qualification
(electronic bk.)
Qualification
(electronic bk.)
(Number (ISBN
9789811634208
(Number (ISBN
9811634203
Erroneous ISBN
981163419X
Erroneous ISBN
9789811634192
INTERNATIONAL STANDARD MUSIC NUMBER
(Number (ISMN
10.1007/978-981-16-3420-8
NATIONAL BIBLIOGRAPHY NUMBER
Number
15137
OTHER SYSTEM CONTROL NUMBERS
System Control Number
(OCoLC)1301453626
Cancelled or Invalid Control Number
(OCoLC)1302009110
LANGUAGE OF THE ITEM
.Language of Text, Soundtrack etc
انگلیسی
TITLE AND STATEMENT OF RESPONSIBILITY
Title Proper
Distributed machine learning and gradient optimization /
First Statement of Responsibility
Jiawei Jiang, Bin Cui, Ce Zhang.
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
Singapore :
Name of Publisher, Distributor, etc.
Springer,
Date of Publication, Distribution, etc.
2022.
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
169p.
SERIES
Series Title
Big data management.
NOTES PERTAINING TO BINDING AND AVAILABILITY
Text of Note
Available to OhioLINK libraries.
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
Text of Note
Includes bibliographical references.
CONTENTS NOTE
Text of Note
1 Introduction -- 2 Basics of Distributed Machine Learning -- 3 Distributed Gradient Optimization Algorithms -- 4 Distributed Machine Learning Systems -- 5 Conclusion.
0
SUMMARY OR ABSTRACT
Text of Note
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.
OTHER EDITION IN ANOTHER MEDIUM
International Standard Book Number
9789811634192
Bibliographic Record Identifier
(OCoLC)1250511313.
TOPICAL NAME USED AS SUBJECT
Entry Element
Machine learning.
Entry Element
Mathematical optimization.
(SUBJECT CATEGORY (Provisional
Subject Category Subdivision Code
COM004000
System Code
bisacsh
DEWEY DECIMAL CLASSIFICATION
Number
006
.
3/1
Edition
23
LIBRARY OF CONGRESS CLASSIFICATION
Class number
Q325
.
5
Book number
.
J5
2022
PERSONAL NAME - PRIMARY RESPONSIBILITY
Entry Element
Jiang, Jiawei,
PERSONAL NAME - SECONDARY RESPONSIBILITY
Entry Element
Cui, Bin,
Entry Element
Zhang, Ce,
CORPORATE BODY NAME - SECONDARY RESPONSIBILITY
Entry Element
Ohio Library and Information Network.
ORIGINATING SOURCE
Agency
کتابخانه مرکزی و مرکز اطلاع رسانی دانشگاه
Date of Transaction
20231009062106.0
Cataloguing Rules (Descriptive Conventions))
rda
ELECTRONIC LOCATION AND ACCESS
Date and Hour of Consultation and Access
Distributed Machine Learning and Gradient Optimization-Springer (2022).pdf
Uniform Resource Identifier
https://rave.ohiolink.edu/ebooks/ebc2/9789811634208
Uniform Resource Identifier
https://link.springer.com/10.1007/978-981-16-3420-8
Uniform Resource Identifier
http://proxy.ohiolink.edu:9099/login?url=https://link.springer.com/10.1007/978-981-16-3420-8
Public note
Connect to resource
Public note
Connect to resource
Public note
Connect to resource (off-campus)
BL
270410
Y
