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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...
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| Auteurs principaux : | , , |
|---|---|
| Format : | Livre |
| Langue : | anglais |
| Titre complet : | Deep learning / Ian Goodfellow, Yoshua Bengio and Aaron Courville |
| Publié : |
Cambridge (Mass.), London :
the MIT Press
, C 2016 |
| Description matérielle : | 1 vol. (XXII-775 p.) |
| Collection : | Adaptative computation and machine learning series |
| Sujets : | |
| Documents associés : | Autre format:
Deep learning |
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| 200 | 1 | |a Deep learning |f Ian Goodfellow, Yoshua Bengio and Aaron Courville | |
| 214 | 0 | |a Cambridge (Mass.) |a London |c the MIT Press | |
| 214 | 4 | |d C 2016 | |
| 215 | |a 1 vol. (XXII-775 p.) |c ill. en noir et en coul., graph., couv. ill. en coul. |d 24 cm | ||
| 225 | 0 | |a Adaptive computation and machine learning | |
| 300 | |a Liste des errata sur le lien suivant https://www.deeplearningbook.org/ | ||
| 320 | |a Bibliogr. p. [711]-766. Index | ||
| 330 | |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. |2 4e de couv. | ||
| 359 | 2 | |b I. Applied math and machine learning basics |c 2. Linear algebra |c 3. Probability and information theory |c 4. Numerical computation |c 5. Machine learning basics |b II. Deep networks : modern practices |c 6. Deep feedforward networks |c 7. Regularization for deep learning |c 8. Optimization for training deep models |c 9. Convolutional networks |c 10. Sequence modeling : recurrent and recursive nets |c 11. Practical methodology |c 12. Applications |b III. Deep learning research |c 13. Linear factor models |c 14. Autoencoders |c 15. Representation learning |c 16. Structured probabilistic models for deep learning |c 17. Monte Carlo methods |c 18. Confronting the partition function |c 19. Approximate inference |c 20. Deep generative models | |
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| 452 | | | |0 25087847X |t Deep learning |f Ian Goodfellow, Yoshua Bengio and Aaron Courville |d 2016 |c Cambridge, Massachusetts |n The MIT Press |p 1 online resource (xxii, 775 pages) |s Adaptive computation and machine learning |y 978-0-262-33737-3 | |
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