Book Image

R Machine Learning Projects

By : Dr. Sunil Kumar Chinnamgari
Book Image

R Machine Learning Projects

By: Dr. Sunil Kumar Chinnamgari

Overview of this book

R is one of the most popular languages when it comes to performing computational statistics (statistical computing) easily and exploring the mathematical side of machine learning. With this book, you will leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. This book will help you test your knowledge and skills, guiding you on how to build easily through to complex machine learning projects. You will first learn how to build powerful machine learning models with ensembles to predict employee attrition. Next, you’ll implement a joke recommendation engine and learn how to perform sentiment analysis on Amazon reviews. You’ll also explore different clustering techniques to segment customers using wholesale data. In addition to this, the book will get you acquainted with credit card fraud detection using autoencoders, and reinforcement learning to make predictions and win on a casino slot machine. By the end of the book, you will be equipped to confidently perform complex tasks to build research and commercial projects for automated operations.
Table of Contents (12 chapters)
The Road Ahead

Exploring recurrent neural networks

Recurrent neural networks (RNNs) are a family of neural networks for processing sequential data. RNNs are generally used to implement language models. We, as humans, base much of our language understanding on the context. For example, let's consider the sentence Christmas falls in the month of --------. It is easy to fill in the blank with the word December. The essential idea here is that there is information about the last word encoded in the previous elements of the sentence.

The central theme behind the RNN architecture is to exploit the sequential structure of the data. As the name suggests, RNNs operate in a recurrent way. Essentially, this means that the same operation is performed for every element of a sequence or sentence, with its output depending on the current input and the previous operations.

An RNN works by looping an output...