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An Introduction to Deep Learning

Ludovic Arnold 1 Sébastien Rebecchi 1 Sylvain Chevallier 2 Hélène Paugam-Moisy 3 
1 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
3 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
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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Submitted on : Friday, August 5, 2016 - 1:07:40 PM
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  • HAL Id : hal-01352061, version 1


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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