Book Image

Big Data Analytics with R

By : Simon Walkowiak
Book Image

Big Data Analytics with R

By: Simon Walkowiak

Overview of this book

Big Data analytics is the process of examining large and complex data sets that often exceed the computational capabilities. R is a leading programming language of data science, consisting of powerful functions to tackle all problems related to Big Data processing. The book will begin with a brief introduction to the Big Data world and its current industry standards. With introduction to the R language and presenting its development, structure, applications in real world, and its shortcomings. Book will progress towards revision of major R functions for data management and transformations. Readers will be introduce to Cloud based Big Data solutions (e.g. Amazon EC2 instances and Amazon RDS, Microsoft Azure and its HDInsight clusters) and also provide guidance on R connectivity with relational and non-relational databases such as MongoDB and HBase etc. It will further expand to include Big Data tools such as Apache Hadoop ecosystem, HDFS and MapReduce frameworks. Also other R compatible tools such as Apache Spark, its machine learning library Spark MLlib, as well as H2O.
Table of Contents (16 chapters)
Big Data Analytics with R
About the Author
About the Reviewers

Neural Networks with H2O on Hadoop with R

In this part of the chapter we will only briefly introduce you to a very extensive topic of Artificial Neural Networks (ANN) and their implementation in the h2o package for distributed machine learning in R. As a precise explanation of concepts related to Neural Networks goes far beyond the scope of this book, we will limit the theory to the bare minimum and guide you through a practical application of a simple Neural Network model performed on our flight data.

How do Neural Networks work?

It is very likely that at some point on your data analytical journey, you have come across the concept of Neural Networks in one way or another. Even if you are unaware of their mathematical underpinnings and theoretical assumptions, you've probably heard and followed many news stories about the advances of artificial intelligence and systems built on Neural Networks and their multi-layered versions in the form of Deep Learning algorithms.

As the name suggests, the...