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Distributed machine learning and gradient optimization

Record Type:	Electronic resources : Monograph/item
Title/Author:	Distributed machine learning and gradient optimizationby Jiawei Jiang, Bin Cui, Ce Zhang.
Author:	Jiang, Jiawei.
other author:	Cui, Bin.
Published:	Singapore :Springer Singapore :2022.
Description:	xi, 169 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-981-16-3420-8
ISBN:	9789811634208$q(electronic bk.)

Distributed machine learning and gradient optimization
Jiang, Jiawei.

Distributed machine learning and gradient optimization[electronic resource] /by Jiawei Jiang, Bin Cui, Ce Zhang. - Singapore :Springer Singapore :2022. - xi, 169 p. :ill., digital ;24 cm. - 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$q(electronic bk.)

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

188639
Machine learning.

LC Class. No.: Q325.5 / .J53 2022

Dewey Class. No.: 006.31

Distributed machine learning and gradient optimization
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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.
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