Skip to Main content Skip to Navigation
Conference papers

An Introduction to Deep Learning

Ludovic Arnold 1 Sébastien Rebecchi 1 Sylvain Chevallier 2 Hélène Paugam-Moisy 3
1 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
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.
Complete list of metadatas

Cited literature [44 references]  Display  Hide  Download
Contributor : Sylvain Chevallier <>
Submitted on : Friday, August 5, 2016 - 1:07:40 PM
Last modification on : Wednesday, July 8, 2020 - 12:43:50 PM


Explicit agreement for this submission


  • 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⟩



Record views


Files downloads