Distributed machine learning and gradient optimization
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 gr…
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- Full title :Distributed machine learning and gradient optimization / Jiawei Jiang, Bin Cui, Ce Zhang.
Summary
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.
Read More ↓Edition Statements
- Publication Date:2022
Publication Statements
- Publisher(s):Springer
- Place of Publication:Singapore, Singapore
- Language:eng
- Publication Date:2022
The Physical Object
- Description:xi, 169 pages : : illustrations (chiefly color) ; : 25 cm.
Edition Identifiers
- ISBN:9789811634192, 981163419X
- oclc:on1250511313
- LC Classification:Q325.5
- Dewey Decimal:006.3/1
Table of Contents
1 Introduction -- 2 Basics of Distributed Machine Learning -- 3 Distributed Gradient Optimization Algorithms -- 4 Distributed Machine Learning Systems -- 5 Conclusion. .
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