Learning deep architectures for AI by Yoshua Bengio.
Tipo de material:![Texto](/opac-tmpl/lib/famfamfam/BK.png)
Contenidos:
1. Introduction -- 2. Theoretical advantages of deep architectures -- 3. Local vs non-local generalization -- 4. Neural networks for deep architectures -- 5. Energy-based models and Boltzmann machines -- 6. Greedy layer-wise training of deep architectures -- 7. Variants of RBMs and auto-encoders -- 8. Stochastic variational bounds for joint optimization of DBN layers -- 9. Looking forward -- 10. Conclusion
Tipo de ítem | Biblioteca actual | Colección | número de clasificación | Copia número | Estado | Notas | Fecha de vencimiento | Código de barras |
---|---|---|---|---|---|---|---|---|
![]() |
Biblioteca Campus San Joaquín | Colección General | 006.31 B465 (Navegar estantería(Abre debajo)) | 1 | Disponible | 3560902046701 | ||
![]() |
Biblioteca Campus San Joaquín | Colección General | 006.31 B465 (Navegar estantería(Abre debajo)) | 2 | Disponible | 3560902046704 | ||
![]() |
Biblioteca Campus San Joaquín | Colección General | 006.31 B465 (Navegar estantería(Abre debajo)) | 3 | Disponible | 3560902046702 |
Incluye bibliografia (p. : 113.127)
1. Introduction -- 2. Theoretical advantages of deep architectures -- 3. Local vs non-local generalization -- 4. Neural networks for deep architectures -- 5. Energy-based models and Boltzmann machines -- 6. Greedy layer-wise training of deep architectures -- 7. Variants of RBMs and auto-encoders -- 8. Stochastic variational bounds for joint optimization of DBN layers -- 9. Looking forward -- 10. Conclusion
9