(electronic bk.)
(electronic bk.)
9789811634208
9811634203
981163419X
9789811634192
10.1007/978-981-16-3420-8
15137
(OCoLC)1301453626
(OCoLC)1302009110
انگلیسی
Distributed machine learning and gradient optimization /
Jiawei Jiang, Bin Cui, Ce Zhang.
Singapore :
Springer,
2022.
169p.
Big data management.
Available to OhioLINK libraries.
Includes bibliographical references.
1 Introduction -- 2 Basics of Distributed Machine Learning -- 3 Distributed Gradient Optimization Algorithms -- 4 Distributed Machine Learning Systems -- 5 Conclusion.
0
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.
9789811634192
(OCoLC)1250511313.
Machine learning.
Mathematical optimization.
COM004000
bisacsh
006
.
3/1
23
Q325
.
5
.
J5
2022
Jiang, Jiawei,
Cui, Bin,
Zhang, Ce,
Ohio Library and Information Network.
کتابخانه مرکزی و مرکز اطلاع رسانی دانشگاه
20231009062106.0
rda
Distributed Machine Learning and Gradient Optimization-Springer (2022).pdf
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