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Neural Networks and learning machines / Simon Haykin

Public ISBD Titre : Neural Networks and learning machines Type de document : Livre Auteurs : Simon Haykin, Auteur Mention d'édition : 3ème Editeur : Pearson Année de publication : 2022 Langues : Anglais (eng) Index. décimale : 681.5.01 Principes et théorie de l'automation Résumé : This third edition of a classic book presents a comprehensive treatment of neural networks and learning machines. These two pillars that are closely related. The book has been revised extensively to provide an up-to-date treatment of a subject that is continually growing in importance. Distinctive features of the book include: • On-line learning algorithms rooted in stochastic gradient descent; small-scale and large-scale learning problems. • Kernel methods, including support vector machines, and the representer theorem. • Information-theoretic learning models, including copulas, independent components analysis (ICA), coherent ICA, and information bottleneck. • Stochastic dynamic programming, including approximate and neurodynamic procedures. • Sequential state-estimation algorithms, including Kalman and particle filters. • Recurrent neural networks trained using sequential-state estimation algorithms. • Insightful computer-oriented experiments. Neural Networks and learning machines [Livre] / Simon Haykin, Auteur  . -  3ème . - Pearson, 2022. Langues : Anglais (eng) Index. décimale : 681.5.01 Principes et théorie de l'automation Résumé : This third edition of a classic book presents a comprehensive treatment of neural networks and learning machines. These two pillars that are closely related. The book has been revised extensively to provide an up-to-date treatment of a subject that is continually growing in importance. Distinctive features of the book include: • On-line learning algorithms rooted in stochastic gradient descent; small-scale and large-scale learning problems. • Kernel methods, including support vector machines, and the representer theorem. • Information-theoretic learning models, including copulas, independent components analysis (ICA), coherent ICA, and information bottleneck. • Stochastic dynamic programming, including approximate and neurodynamic procedures. • Sequential state-estimation algorithms, including Kalman and particle filters. • Recurrent neural networks trained using sequential-state estimation algorithms. • Insightful computer-oriented experiments.

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
90488	681.5.01 HAY	Livre	ECAM	automatique	Disponible

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