Distributed Machine Learning and Gradient Optimization

Distributed Machine Learning and Gradient Optimization  (English, Hardcover, Jiang Jiawei)

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    Highlights
    • Language: English
    • Binding: Hardcover
    • Publisher: Springer Verlag, Singapore
    • Genre: Mathematics
    • ISBN: 9789811634192
    • Pages: 169
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  • 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.
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    Book Details
    Imprint
    • Springer Verlag, Singapore
    Dimensions
    Height
    • 235 mm
    Length
    • 155 mm
    Weight
    • 448 gr
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