Some of the pioneering advancements in neural networks research in the last decade have opened up a new frontier in machine learning that is generally called by the name **deep learning** (references 5 and 7 in the *References* section of this chapter). The general definition of deep learning is, *a class of machine learning techniques, where many layers of information processing stages in hierarchical supervised architectures are exploited for unsupervised feature learning and for pattern analysis/classification. The essence of deep learning is to compute hierarchical features or representations of the observational data, where the higher-level features or factors are defined from lower-level ones* (reference 8 in the *References* section of this chapter). Although there are many similar definitions and architectures for deep learning, two common elements in all of them are: *multiple layers of nonlinear information processing* and *supervised or unsupervised learning...*

#### Learning Bayesian Models with R

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#### Learning Bayesian Models with R

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#### Overview of this book

Table of Contents (16 chapters)

Learning Bayesian Models with R

Credits

About the Author

About the Reviewers

www.PacktPub.com

Preface

Free Chapter

Introducing the Probability Theory

The R Environment

Introducing Bayesian Inference

Machine Learning Using Bayesian Inference

Bayesian Regression Models

Bayesian Classification Models

Bayesian Models for Unsupervised Learning

Bayesian Neural Networks

Bayesian Modeling at Big Data Scale

Index

Customer Reviews