Titre :	Deep learning
Type de document :	texte imprimé
Auteurs :	Ian Goodfellow, Auteur ; Yoshua Bengio, Auteur ; Aaron Courville, Auteur
Editeur :	Cambridge (Mass.) : the MIT Press
Année de publication :	copyright 2016
Collection :	Adaptative computation and machine learning series
Importance :	1 vol. (775 p.)
Présentation :	ill. en noir et en coul., couv. ill. en coul.
Format :	24 cm
ISBN/ISSN/EAN :	978-0-262-03561-3
Note générale :	978-0-262-03561-3
Langues :	Anglais (eng)
Catégories :	Informatique
Mots-clés :	Apprentissage automatique Apprentissage profond Modèles mathématiques Intelligence artificielle Analyse multivariée
Index. décimale :	006.3 Intelligence artificielle
Résumé :	La 4e de couverture indique : "Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones ; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning."
Note de contenu :	Sommaire I. Applied math and machine learning basics 2. Linear algebra 3. Probability and information theory 4. Numerical computation 5. Machine learning basics II. Deep networks : modern practices 6. Deep feedforward networks 7. Regularization for deep learning 8. Optimization for training deep models 9. Convolutional networks 10. Sequence modeling : recurrent and recursive nets 11. Practical methodology 12. Applications III. Deep learning research 13. Linear factor models 14. Autoencoders 15. Representation learning 16. Structured probabilistic models for deep learning 17. Monte Carlo methods 18. Confronting the partition function 19. Approximate inference 20. Deep generative models
Côte titre :	Fs/23308-23309

Deep learning [texte imprimé] / Ian Goodfellow, Auteur ; Yoshua Bengio, Auteur ; Aaron Courville, Auteur . - Cambridge (Mass.) : the MIT Press, copyright 2016 . - 1 vol. (775 p.) : ill. en noir et en coul., couv. ill. en coul. ; 24 cm. - (Adaptative computation and machine learning series) .
ISBN : 978-0-262-03561-3
978-0-262-03561-3
Langues : Anglais (eng)

Catégories :	Informatique
Mots-clés :	Apprentissage automatique Apprentissage profond Modèles mathématiques Intelligence artificielle Analyse multivariée
Index. décimale :	006.3 Intelligence artificielle
Résumé :	La 4e de couverture indique : "Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones ; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning."
Note de contenu :	Sommaire I. Applied math and machine learning basics 2. Linear algebra 3. Probability and information theory 4. Numerical computation 5. Machine learning basics II. Deep networks : modern practices 6. Deep feedforward networks 7. Regularization for deep learning 8. Optimization for training deep models 9. Convolutional networks 10. Sequence modeling : recurrent and recursive nets 11. Practical methodology 12. Applications III. Deep learning research 13. Linear factor models 14. Autoencoders 15. Representation learning 16. Structured probabilistic models for deep learning 17. Monte Carlo methods 18. Confronting the partition function 19. Approximate inference 20. Deep generative models
Côte titre :	Fs/23308-23309