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Distributed Machine Learning and Gradient - Research Book

Distributed Machine Learning and Gradient - Research Book

Top Research Book in Distributed Machine Learning and Gradient

Author(s) Name:  Jiawei Jiang, Bin Cui, Ce Zhang

About the Book:

   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.

Table of Contents

  • Introduction
  • Basics of Distributed Machine Learning
  • Distributed Gradient Optimization Algorithms
  • Distributed Machine Learning Systems
  • Conclusion
  • ISBN:  978-981-16-3420-8

    Publisher:  Springer

    Year of Publication:  2022

    Book Link:  Home Page Url