The previous chapters covered a bit of the theory behind neural networks and used some neural network packages in R. Now it is time to dive in and look at training deep learning models. In this chapter, we will explore how to train and build feedforward neural networks, which are the most common type of deep learning model. We will use MXNet to build deep learning models to perform classification and regression using a retail dataset.
This chapter will cover the following topics:
- Getting started with deep feedforward neural networks
- Common activation functions – rectifiers, hyperbolic tangent, and maxout
- Introduction to the MXNet deep learning library
- Use case – Using MXNet for classification and regression
