Distributed Machine Learning and Gradient Optimization (Big Data Management)

Jiang, Jiawei/ Cui, Bin/ Zhang, Ce

Springer Verlag, Singapore（2023/02発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 169 p.
• 言語 ENG
• 商品コード 9789811634222
• DDC分類 006.31

Full Description

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 appealto 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.