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Elargir la recherche         Sur le même sujet :     Apprentissage automatique     Algorithmes     Machine learning     Computer algorithms.     Parcourir le catalogue     par auteur:      Shalev-Shwartz , Shai      Ben-David , Shai       Rechercher sur Internet     Localiser dans une autre bibliothèque (SUDOC) (PPN ou ISBN ou ISSN)     Aperçu dans Google Books     Affichage MARC

Auteur :  Shalev-Shwartz , Shai Ben-David , Shai Titre :  Understanding machine learning : from theory to algorithms , Shai Shalev-Shwartz,... Shai Ben-David,... Editeur :  New York : Cambridge University Press C 2014 Description :  1 vol. (XVI-397 p.) : ill., couv. ill. en coul. ; 26 cm ISBN:  978-1-107-05713-5 , rel. Notes :  Bibliogr. p. 385-393. Index Autre tirage: 2018 La quatrième de couverture indique : "Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering." Sujet :  Apprentissage automatique Algorithmes Machine learning Computer algorithms.   Exemplaires Site Emplacement Cote Type de prêt Statut   Ensea Espace bibliothèque ENSEA 006.31 SHA Empruntable Disponible

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