领域(数学)
人工智能
计算机科学
深度学习
点(几何)
基础(证据)
数学教育
数据科学
机器学习
管理科学
数学
工程类
地理
几何学
考古
纯数学
作者
Philipp Grohs,Philipp Grohs,Julius Berner,Kenichi Kawaguchi,Ingo Gühring,René Vidal,Wojciech Samek,Thomas Merkh,A. Aberdam,Joan Bruna,Alexandros G. Dimakis,Jiequn Han Weinan E,Yoav Levine
出处
期刊:Cambridge University Press eBooks
[Cambridge University Press]
日期:2022-11-29
被引量:46
标识
DOI:10.1017/9781009025096
摘要
In recent years the development of new classification and regression algorithms based on deep learning has led to a revolution in the fields of artificial intelligence, machine learning, and data analysis. The development of a theoretical foundation to guarantee the success of these algorithms constitutes one of the most active and exciting research topics in applied mathematics. This book presents the current mathematical understanding of deep learning methods from the point of view of the leading experts in the field. It serves both as a starting point for researchers and graduate students in computer science, mathematics, and statistics trying to get into the field and as an invaluable reference for future research.
科研通智能强力驱动
Strongly Powered by AbleSci AI