You might also like

More about Distributed Machine Learning and Gradient Optimization

• Book details
• Synopsis
• Trending among others

You get 387 loyalty points

Book synopsis

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.

Book details

Book category Books in English Computing & information technology Computer science Artificial intelligence

• Full title: Distributed Machine Learning and Gradient Optimization
• Author: Jiawei Jiang, Bin Cui, Ce Zhang
• Language: English
• Binding: Paperback
• Number of pages: 169
• EAN: 9789811634222
• ID: 42881568
• Publisher: Springer, Berlin
• Weight: 267 g
• Dimensions: 235 × 155 mm
• Date of publishing: 10. March 2023

Trending among others

---

---

---