New Machine Learning Findings from Swiss Federal Institute of Technology Describ ed (Stochastic Gradient Descent Without Full Data Shuffle: With Applications To In-database Machine Learning and Deep Learning Systems)

Robotics & Machine Learning Daily News2024，Issue(MAY.15) ：83-85.

来源：

NETL
NSTL

New Machine Learning Findings from Swiss Federal Institute of Technology Describ ed (Stochastic Gradient Descent Without Full Data Shuffle: With Applications To In-database Machine Learning and Deep Learning Systems)

扫码查看

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting out of Zurich, Switzerland, b y NewsRx editors, research stated, “Modern machine learning (ML) systems commonl y use stochastic gradient descent (SGD) to train ML models. However, SGD relies on random data order to converge, which usually requires a full data shuffle.”

Key words

Zurich/Switzerland/Europe/Cyborgs/Em erging Technologies/Machine Learning/Swiss Federal Institute of Technology

引用本文复制引用

出版年

2024

Robotics & Machine Learning Daily News

ISSN：

段落导航