In this chapter, we learned about an important class of machine learning model, namely neural networks, and their Bayesian implementation. These models are inspired by the architecture of the human brain and they continue to be an area of active research and development. We also learned one of the latest advances in neural networks that is called deep learning. It can be used to solve many problems such as computer vision and natural language processing that involves highly cognitive elements. The artificial intelligent systems using deep learning were able to achieve accuracies comparable to human intelligence in tasks such as speech recognition and image classification. With this chapter, we have covered important classes of Bayesian machine learning models. In the next chapter, we will look at a different aspect: large scale machine learning and some of its applications in Bayesian models.
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