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

Implementing a deep learning network for handwritten digit recognition

The mxnet library offers several functions that enable us to define the layers and activations that comprise the deep learning network. The definition of layers, the usage of activation functions, and the number of neurons to be used in each of the hidden layers is generally termed the network architecture. Deciding on the network architecture is more of an art than a science. Often, several iterations of experiments may be needed to decide on the right architecture for the problem. We call it an art as there are no exact rules for finding the ideal architecture. The number of layers, neurons in these layers, and the type of layers are pretty much decided through trial and error.

In this section, we'll build a simple deep learning network with three hidden layers. Here is the general architecture of our...