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Learning Bayesian Models with R

Learning Bayesian Models with R

By : Hari Manassery Koduvely
3.4 (7)
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Learning Bayesian Models with R

Learning Bayesian Models with R

3.4 (7)
By: Hari Manassery Koduvely

Overview of this book

Bayesian Inference provides a unified framework to deal with all sorts of uncertainties when learning patterns form data using machine learning models and use it for predicting future observations. However, learning and implementing Bayesian models is not easy for data science practitioners due to the level of mathematical treatment involved. Also, applying Bayesian methods to real-world problems requires high computational resources. With the recent advances in computation and several open sources packages available in R, Bayesian modeling has become more feasible to use for practical applications today. Therefore, it would be advantageous for all data scientists and engineers to understand Bayesian methods and apply them in their projects to achieve better results. Learning Bayesian Models with R starts by giving you a comprehensive coverage of the Bayesian Machine Learning models and the R packages that implement them. It begins with an introduction to the fundamentals of probability theory and R programming for those who are new to the subject. Then the book covers some of the important machine learning methods, both supervised and unsupervised learning, implemented using Bayesian Inference and R. Every chapter begins with a theoretical description of the method explained in a very simple manner. Then, relevant R packages are discussed and some illustrations using data sets from the UCI Machine Learning repository are given. Each chapter ends with some simple exercises for you to get hands-on experience of the concepts and R packages discussed in the chapter. The last chapters are devoted to the latest development in the field, specifically Deep Learning, which uses a class of Neural Network models that are currently at the frontier of Artificial Intelligence. The book concludes with the application of Bayesian methods on Big Data using the Hadoop and Spark frameworks.
Table of Contents (11 chapters)
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10
Index

Computing clusters on the cloud


In order to process large datasets using Hadoop and associated R packages, one needs a cluster of computers. In today's world, it is easy to get using cloud computing services provided by Amazon, Microsoft, and others. One needs to pay only for the amount of CPU and storage used. No need for upfront investments on infrastructure. The top four cloud computing services are AWS by Amazon, Azure by Microsoft, Compute Cloud by Google, and Bluemix by IBM. In this section, we will discuss running R programs on AWS. In particular, you will learn how to create an AWS instance; install R, RStudio, and other packages in that instance; develop and run machine learning models.

Amazon Web Services

Popularly known as AWS, Amazon Web Services started as an internal project in Amazon in 2002 to meet the dynamic computing requirements to support their e-commerce business. This grew as an infrastructure as a service and in 2006 Amazon launched two services to the world, Simple...

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