Deep Learning in neural networks: An overview

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Abstract

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.
Original languageEnglish (US)
Pages (from-to)85-117
Number of pages33
JournalNeural Networks
Volume61
DOIs
StatePublished - Jan 1 2015
Externally publishedYes

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