| Titre : |
Deep Learning |
| Type de document : |
texte imprimé |
| Auteurs : |
Ian J. GOODFELLOW, Auteur ; Yoshua BENGIO, Auteur ; Aaron C. COURVILLE, Auteur |
| Editeur : |
The MIT Press |
| Année de publication : |
2016 |
| Collection : |
Adaptative Computation and Machine Learning |
| Importance : |
775 p. |
| ISBN/ISSN/EAN : |
978-0-262-03561-3 |
| Note générale : |
Contents
Website
Acknowledgements
Notation
Bibliography
Index |
| Catégories : |
Apprentissage automatique
Apprentissage profond
Information, Théorie de l'
Intelligence artificielle
Monte Carlo, Méthode de
|
| Index. décimale : |
006.3 Intelligence artificielle |
| Résumé : |
Introduction
APPLIED MATH AND MACHINE LEARNING BASICS
Linear Algebra
Probability and Information Theory
Numerical Computation
Machine Learning Basics
DEEP NETWORKS: MODERN PRACTICES
Deep Feedforward Networks
Regularization for Deep Learning
Optimization for Training Deep Models
Convolutional Networks
Sequence Modelling: Recurrent and Recursive Nets
Practical Methodology
Applications
DEEP LEARNING RESEARCH
Linear Factor Models
Autoencoders
Representation Learning
Structured Probabilistic Models for Deep Learning
Monte Carlo Methods
Confronting the Partition Function
Approximate Inference
Deep Generative Models
|
