Deep learning in neural networks: An overview

In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the d...

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Bibliographic Details
Published in:Neural networks Vol. 61; pp. 85 - 117
Main Author: Schmidhuber, Jürgen
Format: Journal Article
Language:English
Published: United States Elsevier Ltd 01.01.2015
Subjects:
Artificial Intelligence - classification
Artificial Intelligence - standards
Artificial Intelligence - trends
Deep learning
Evolutionary computation
Reinforcement learning
Supervised learning
Unsupervised learning
Deep learning
Supervised learning
Unsupervised learning
Reinforcement learning
Evolutionary computation
ISSN:0893-6080, 1879-2782, 1879-2782
Online Access:Get full text
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Summary:In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
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ISSN:0893-6080
1879-2782
1879-2782
DOI:10.1016/j.neunet.2014.09.003