We a good story
Quick delivery in the UK

Distributed Machine Learning and Gradient Optimization

About 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 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.

Show more
  • Language:
  • English
  • ISBN:
  • 9789811634222
  • Binding:
  • Paperback
  • Pages:
  • 184
  • Published:
  • February 24, 2023
  • Edition:
  • 23001
  • Dimensions:
  • 155x11x235 mm.
  • Weight:
  • 289 g.
Delivery: 2-4 weeks
Expected delivery: January 24, 2025
Extended return policy to January 30, 2025
  •  

    Cannot be delivered before Christmas.
    Buy now and print a gift certificate

Description of 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 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.

User ratings of Distributed Machine Learning and Gradient Optimization



Find similar books
The book Distributed Machine Learning and Gradient Optimization can be found in the following categories:

Join thousands of book lovers

Sign up to our newsletter and receive discounts and inspiration for your next reading experience.