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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 |
- 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