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Distributed Machine Learning and Gradient Optimization / by Jiawei Jiang, Bin Cui, Ce Zhang.
- Format:
- Book
- Author/Creator:
- Jiang, Jiawei, Author.
- Cui, Bin, Author.
- Zhang, Ce, Author.
- Series:
- Computer Science (SpringerNature-11645)
- Big Data Management, 2522-0187
- Language:
- English
- Subjects (All):
- Machine learning.
- Data mining.
- Database management.
- Machine Learning.
- Data Mining and Knowledge Discovery.
- Database Management.
- Local Subjects:
- Machine Learning.
- Data Mining and Knowledge Discovery.
- Database Management.
- Physical Description:
- 1 online resource (XI, 169 pages) : 1 illustrations
- Edition:
- 1st ed. 2022.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Singapore : Springer Nature Singapore : Imprint: Springer, 2022.
- System Details:
- text file PDF
- 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.
- Contents:
- 1 Introduction
- 2 Basics of Distributed Machine Learning
- 3 Distributed Gradient Optimization Algorithms
- 4 Distributed Machine Learning Systems
- 5 Conclusion. .
- Other Format:
- Printed edition:
- ISBN:
- 978-981-16-3420-8
- 9789811634208
- Access Restriction:
- Restricted for use by site license.
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