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

Problems and solutions to gradients in RNN

RNNs are not perfect, there are two main issues namely exploding gradients and vanishing gradients that they suffer from. To understand the issues, let's first understand what a gradient means. A gradient is a partial derivative with respect to its inputs. In simple layman's terms, a gradient measures how much the output of a function changes, if one were to change the inputs a little bit.

Exploding gradients

Exploding gradients relate to a situation where the BPTT algorithm assigns an insanely high importance to the weights, without a rationale. The problem results in an unstable network. In extreme situations, the values of weights can become so large that the values overflow...