Book Image

Advanced Machine Learning with R

By : Cory Lesmeister, Dr. Sunil Kumar Chinnamgari
Book Image

Advanced Machine Learning with R

By: Cory Lesmeister, Dr. Sunil Kumar Chinnamgari

Overview of this book

R is one of the most popular languages when it comes to exploring the mathematical side of machine learning and easily performing computational statistics. This Learning Path shows you how to leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. You’ll work through realistic projects such as building powerful machine learning models with ensembles to predict employee attrition. Next, you’ll explore different clustering techniques to segment customers using wholesale data and even apply TensorFlow and Keras-R for performing advanced computations. Each chapter will help you implement advanced machine learning algorithms using real-world examples. You’ll also be introduced to reinforcement learning along with its use cases and models. Finally, this Learning Path will provide you with a glimpse into how some of these black box models can be diagnosed and understood. By the end of this Learning Path, you’ll be equipped with the skills you need to deploy machine learning techniques in your own projects.
Table of Contents (30 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

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 network:

  1. The input layer is defined as the initial layer in the network. The mx.symbol.Variable MXNet function defines...