R Deep Learning Essentials

In this chapter we will explore how to train and build deep prediction models. We will focus on feedforward neural networks, which are perhaps the most common type and a good starting point. This chapter will cover the following topics: Getting started with deep feedforward neural networks Common activation functions: rectifiers, hyperbolic tangent, and maxout Picking hyperparameters Training and predicting new data from a deep neural network Use case – training a deep neural network for automatic classification In this chapter, we will not use any new packages. The only requirements are to source the checkpoint.R file to set up the R environment for the rest of the code shown and to initialize the H2O cluster. …

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