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An Introduction to Deep Learning

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Abstract

The deep learning paradigm tackles problems on which shallow architectures (e.g. SVM) are affected by the curse of dimensionality. As part of a two-stage learning scheme involving multiple layers of non-linear processing a set of statistically robust features is automatically extracted from the data. The present tutorial introducing the ESANN deep learning special session details the state-of-the-art models and summarizes the current understanding of this learning approach which is a reference for many difficult classification tasks.
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Dates and versions

hal-01352061 , version 1 (05-08-2016)

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  • HAL Id : hal-01352061 , version 1

Cite

Ludovic Arnold, Sébastien Rebecchi, Sylvain Chevallier, Hélène Paugam-Moisy. An Introduction to Deep Learning. European Symposium on Artificial Neural Networks (ESANN), Apr 2011, Bruges, Belgium. ⟨hal-01352061⟩
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