| Titre : |
Deep learning |
| Type de document : |
texte imprimé |
| Auteurs : |
Ian J. Goodfellow (1987-....), Auteur ; Yoshua Bengio (1964-....), Auteur ; Aaron C. Courville |
| Editeur : |
Cambridge, Mass. : MIT Press |
| Année de publication : |
2016, cop. 2016 |
| Collection : |
Adaptive computation and machine learning |
| Importance : |
1 vol. (XXII-775 p.) |
| Présentation : |
ill. en noir et en coul., graph., couv. ill. en coul. |
| Format : |
24 cm |
| ISBN/ISSN/EAN : |
978-0-262-03561-3 |
| Note générale : |
PPN 197682979 |
| Langues : |
Anglais (eng) |
| Tags : |
Apprentissage automatique Apprentissage profond Modèles mathématiques Intelligence artificielle Analyse multivariée Probabilités Information, Théorie de l' Monte-Carlo, Méthode de Machine learning Mathematical models Artificial intelligence Multivariate analysis Information theory Probabilities Monte Carlo methods |
| Index. décimale : |
006.31 Apprentissage automatique (informatique) |
| Résumé : |
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. (4e de couverture) |
| Note de contenu : |
Bibliographie p. [711]-766. - Index p.[767]-775
Sommaire (abrégé) : I- Applied math and machine learning basics (p.27) - 2. Linear algebra (p.29) - 3. Probability and information theory (p.51) - 4. Numerical computation (p.77) - 5. Machine learning basics (p.95) -- II - Deep networks : modern practices (p.161) -- 6. Deep feedforward networks (p.163) - 7. Regularization for deep learning (p.221) -- 8. Optimization for training deep models (p.267) - 9. Convolutional networks (p.321) - 10. Sequence modeling : recurrent and recursive nets (p.363) - 11. Practical methodology (p.409) -- 12. Applications (p.431) -- III- Deep learning research (p.475) - 13. Linear factor models (p.479) - 14. Autoencoders (p.493)- 15. Representation learning (p.517) - 16. Structured probabilistic models for deep learning (p.549) - 17. Monte Carlo methods (p.581) -- 18. Confronting the partition function (p.597) - 19. Approximate inference (p.623) - 20. Deep generative models (p.645) -- Bibliography (p.711-766) -- Index (p.767-775) |
Deep learning [texte imprimé] / Ian J. Goodfellow (1987-....), Auteur ; Yoshua Bengio (1964-....), Auteur ; Aaron C. Courville . - Cambridge, Mass. : MIT Press, 2016, cop. 2016 . - 1 vol. (XXII-775 p.) : ill. en noir et en coul., graph., couv. ill. en coul. ; 24 cm. - ( Adaptive computation and machine learning) . ISBN : 978-0-262-03561-3 PPN 197682979 Langues : Anglais ( eng)
| Tags : |
Apprentissage automatique Apprentissage profond Modèles mathématiques Intelligence artificielle Analyse multivariée Probabilités Information, Théorie de l' Monte-Carlo, Méthode de Machine learning Mathematical models Artificial intelligence Multivariate analysis Information theory Probabilities Monte Carlo methods |
| Index. décimale : |
006.31 Apprentissage automatique (informatique) |
| Résumé : |
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. (4e de couverture) |
| Note de contenu : |
Bibliographie p. [711]-766. - Index p.[767]-775
Sommaire (abrégé) : I- Applied math and machine learning basics (p.27) - 2. Linear algebra (p.29) - 3. Probability and information theory (p.51) - 4. Numerical computation (p.77) - 5. Machine learning basics (p.95) -- II - Deep networks : modern practices (p.161) -- 6. Deep feedforward networks (p.163) - 7. Regularization for deep learning (p.221) -- 8. Optimization for training deep models (p.267) - 9. Convolutional networks (p.321) - 10. Sequence modeling : recurrent and recursive nets (p.363) - 11. Practical methodology (p.409) -- 12. Applications (p.431) -- III- Deep learning research (p.475) - 13. Linear factor models (p.479) - 14. Autoencoders (p.493)- 15. Representation learning (p.517) - 16. Structured probabilistic models for deep learning (p.549) - 17. Monte Carlo methods (p.581) -- 18. Confronting the partition function (p.597) - 19. Approximate inference (p.623) - 20. Deep generative models (p.645) -- Bibliography (p.711-766) -- Index (p.767-775) |
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