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eISBN : 9780262337373

Sommaire :
1. Introduction

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

Bibliography
Index

Langue : Anglais

Collection : ADAPTIVE COMPUTATION AND MACHINE LEARNING SERIES

Localisation : Bibliothèque Campus de Nice

Support : Numérique

Etat : Présent

Propriétaire : Bibliothèque