| Título : |
Deep learning |
| Tipo de documento: |
texto impreso |
| Autores: |
Ian Goodfellow, Autor ; Yoshua Bengio, Autor ; Aaron Courville, Autor |
| Editorial: |
Massachusetts : The Mit press |
| Fecha de publicación: |
2016 |
| Número de páginas: |
775 páginas |
| Il.: |
ilustraciones |
| Dimensiones: |
24 centímetros |
| ISBN/ISSN/DL: |
978-0-262-03561-3 |
| Idioma : |
Español (spa) |
| Clasificación: |
ALGEBRA LINEAL
ENSEÑANZA
INTELIGENCIA ARTIFICIAL
|
| Clasificación: |
006.3 G73 |
| Nota de contenido: |
Linear Algebra. Probability and Information Theory. Numerical Computation. Machine Learning Basics. Deep Feedforward Networks. Regularization for Deep Learning. Optimization for Training Deep Models. Comvolutional Networks. Sequence Modeling: Recurrent and Recursive Nets. Practical Methodology. Applications. Linear Factos Models. Autoencoders. Representation Learning. Structured probabilistic Models for Deep Learning. Monte Carlo Methods. Confronting the Partition Function. Approximate Inference. Deep Generative Models. |
| Link: |
https://pmb.unjbg.edu.pe/opac_css/index.php?lvl=notice_display&id=45687 |
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