《An Introduction to Deep Learning》.pdf

An Introduction to Deep Learning 1,2 1 1 1,3 Ludovic Arnold , Sébastien Rebecchi , Sylvain Chevallier , Hélène Paugam-Moisy 1- Tao, INRIA-Saclay, LRI, UMR8623, Université Paris-Sud 11 F-91405 Orsay, France 2- LIMSI, UMR3251 F-91403 Orsay, France 3- Université Lyon 2, LIRIS, UMR5205 F-69676 Bron, France Abstract. The deep learning paradigm tackles problems on which shal- low 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 ex- tracted 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. 1 Introduction In statistical machine learning, a major issue is the selection of an appropriate feature space where input instances have desired properties for solving a par- ticular problem. For example, in the context of supervised learning for binary classification, it is often required that the two classes are separable by an hy- perplane. In the case where this property is not directly satisfied in the input space, one is given the possibility to map instances into an intermediate feature space where the classes are linearly separable. This intermediate space can ei- ther be specified explicitly by hand-coded features, be defined im