Deep Learning for AI

被引：341

作者：

Bengio, Yoshua ^{[1
]}

Lecun, Yann ^{[2
,3
,4
]}

Hinton, Geoffrey ^{[5
,6
,7
]}

机构：

[1] Univ Montreal, Dept Comp Sci & Operat Res, Montreal, PQ, Canada

[2] Facebook, Menlo Pk, CA USA

[3] New York Univ, Courant Inst Math Sci, New York, NY USA

[4] New York Univ, Ctr Data Sci, New York, NY USA

[5] Vector Inst, Toronto, ON, Canada

[6] Google, Mountain View, CA 94043 USA

[7] Univ Toronto, Comp Sci, Toronto, ON, Canada

来源：

COMMUNICATIONS OF THE ACM | 2021年 / 64卷 / 07期

关键词：

NEURAL-NETWORKS;

D O I：

10.1145/3448250

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

RESEARCH ON ARTIFICIAL neural networks was motivated by the observation that human intelligence emerges from highly parallel networks of relatively simple, non-linear neurons that learn by adjusting the strengths of their connections. This observation leads to a central computational question: How is it possible for networks of this general kind to learn the complicated internal representations that are required for difficult tasks such as recognizing.

引用

页码：58 / 65

页数：8

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