My Account Log in

1 option

Distributed Machine Learning and Gradient Optimization / by Jiawei Jiang, Bin Cui, Ce Zhang.

SpringerLink Books Computer Science (2011-2024) Available online

View online
Format:
Book
Author/Creator:
Jiang, Jiawei, Author.
Cui, Bin, Author.
Zhang, Ce, Author.
Contributor:
SpringerLink (Online service)
Series:
Computer Science (SpringerNature-11645)
Big Data Management, 2522-0187
Language:
English
Subjects (All):
Machine learning.
Data mining.
Database management.
Machine Learning.
Data Mining and Knowledge Discovery.
Database Management.
Local Subjects:
Machine Learning.
Data Mining and Knowledge Discovery.
Database Management.
Physical Description:
1 online resource (XI, 169 pages) : 1 illustrations
Edition:
1st ed. 2022.
Contained In:
Springer Nature eBook
Place of Publication:
Singapore : Springer Nature Singapore : Imprint: Springer, 2022.
System Details:
text file PDF
Summary:
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.
Contents:
1 Introduction
2 Basics of Distributed Machine Learning
3 Distributed Gradient Optimization Algorithms
4 Distributed Machine Learning Systems
5 Conclusion. .
Other Format:
Printed edition:
ISBN:
978-981-16-3420-8
9789811634208
Access Restriction:
Restricted for use by site license.

The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.

Find

Home Release notes

My Account

Shelf Request an item Bookmarks Fines and fees Settings

Guides

Using the Find catalog Using Articles+ Using your account