Deep learning
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...
Gardado en:
| Auteurs principaux: | , , |
|---|---|
| Formato: | Livre papier |
| Idioma: | Anglais |
| Publicado: |
Cambridge (Mass.) ; London :
the MIT Press
C 2016.
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| Series: | Adaptive computation and machine learning
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| Sujets: | |
| Nota: |
Liste des errata sur le lien suivant https://www.deeplearningbook.org/ Autre(s) tirage(s) : 2017 |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Deep learning, Ian Goodfellow, Yoshua Bengio and Aaron Courville, 2016, Cambridge, The MIT Press, 1 online resource (xxii, 775 pages), Adaptive computation and machine learning, 978-0-262-33737-3 |
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| 100 | 1 | |a Goodfellow, Ian J., |d 1987-...., |c chercheur en informatique. | |
| 245 | 1 | 0 | |a Deep learning |c Ian Goodfellow, Yoshua Bengio and Aaron Courville. |
| 260 | |a Cambridge (Mass.) ; |a London : |b the MIT Press. | ||
| 260 | |c C 2016. | ||
| 300 | |a 1 volume (XXII-775 pages) : |b illustrations en noir et en couleurs, graphiques, couverture illustrée en couleurs ; |c 24 cm. | ||
| 490 | 0 | |a Adaptive computation and machine learning | |
| 500 | |a Liste des errata sur le lien suivant https://www.deeplearningbook.org/ | ||
| 500 | |a Autre(s) tirage(s) : 2017 | ||
| 504 | |a Bibliographie pages [711]-766. Index | ||
| 505 | 0 | |a 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 | |
| 520 | |a 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. | ||
| 650 | |a Apprentissage automatique | ||
| 650 | |a Apprentissage profond | ||
| 650 | |a Modèles mathématiques | ||
| 650 | |a Intelligence artificielle | ||
| 650 | |a Analyse multivariée | ||
| 700 | 1 | |a Bengio, Yoshua, |d 1964-...., |c chercheur en informatique. |4 aut | |
| 700 | 1 | |a Courville, Aaron C., |d 19..-...., |c chercheur en informatique. |4 aut | |
| 776 | 0 | |0 25087847X |t Deep learning |f Ian Goodfellow, Yoshua Bengio and Aaron Courville |d 2016 |c Cambridge |n The MIT Press |p 1 online resource (xxii, 775 pages) |s Adaptive computation and machine learning |z 978-0-262-33737-3 | |
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