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|a 004.85
|b GOO
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1 |
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|a Goodfellow, Ian
|9 31472
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| 245 |
1 |
0 |
|a Deep learning
|h [recurso electrónico] /
|c Ian Goodfellow, Yoshua Bengio, Aaron Courville
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| 250 |
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|a 1a ed.
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| 256 |
|
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|a Datos (1 archivo .html)
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| 260 |
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|a s.l. :
|b The MIT Press,
|c [2016]
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| 300 |
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|a recurso en línea (ca.781 p.) :
|b fig. col..
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| 336 |
|
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|a texto (visual)
|2 RDA
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| 337 |
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|a electrónico
|2 RDA
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| 338 |
|
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|a recurso en línea
|2 RDA type carrier
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| 500 |
|
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|a Incluye ejercicios relacionados; diapositivas diseñadas para ampliar conceptos vertidos en algunos capítulos y links hacia comunidades de aprendizaje sobre la temática, lo que incluye videos (en inglés)
|
| 505 |
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|a Part I: Applied Math and machine learning basics: 2. Linear algebra – 3. Probability and information theory – 4. Numerical computation – 5. Machine learning basics – Part II: Modern Practical Deep Networks: 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 -- Part 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
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| 650 |
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7 |
|a Inteligencia artificial
|9 12598
|x Aprendizaje profundo
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| 650 |
|
7 |
|a Aprendizaje automático
|9 8495
|
| 653 |
|
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|a APRENDIZAJE PROFUNDO
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| 653 |
|
|
|a DEEP LEARNING
|
| 653 |
|
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|a MODELOS GENERATIVOS PROFUNDOS
|
| 700 |
1 |
|
|a Bengio, Yoshua
|9 31471
|
| 700 |
1 |
|
|a Courville, Aaron
|9 31470
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| 856 |
|
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|u https://www.deeplearningbook.org/contents/TOC.html#pf9
|y Tabla de contenidos extendida
|z (última consulta: 25/09/2023)
|
| 856 |
|
|
|u https://www.deeplearningbook.org/
|y Acceso al texto completo
|z (última consulta: 25/09/2023)
|
| 942 |
|
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|2 udc
|h 004.85
|c LIB
|6 00485
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| 999 |
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