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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| Glavni autori: | , , |
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| Format: | Livre papier |
| Jezik: | Anglais |
| Izdano: |
Cambridge (Mass.) ; London :
the MIT Press
C 2016.
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| Serija: | Adaptive computation and machine learning
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| Teme: | |
| Bilješka: |
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 |
Sadržaj:
- 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