2016 DeepLearningBook

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Subject Headings: Deep Learning Algorithm; Deep Belief Networks.

Notes

Cited By

2015

Quotes

1 Introduction[1]
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 Feedforward Deep Networks
       7 Regularization
       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 and Auto-Encoders
       14 Representation Learning
       15 The Manifold Perspective on 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 [2]

References

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2016 DeepLearningBookYoshua Bengio
Aaron Courville
Yoav Goldberg
Ian J. Goodfellow
Deep Learning