Introduction to Deep Learning with Keras in R

A step-by-step tutorial

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Table of contents

1. Introduction
2. Environment Setup
3. Dataset
4. Preprocessing
5. Building the neural network
   5.1 Define the layers
   5.2 Compile
   5.3 Fit
6. Test set performances
7. Conclusions
8. References

1. Introduction

Both R and Python are useful and popular tools for Data Science. However, when it comes to Deep Learning, it is most common to find tutorials and guides for Python rather than R.

This post provides a simple Deep Learning example in the R language. It aims at sharing a practical introduction to the subject for R practitioners, using Keras.

2. Environment Setup

In this example, we share code snippets that can be easily copied and pasted on Google Colab¹.

Colab allows anyone to create notebooks in Python or R by writing code through the browser, entirely for free.

We can create a new R notebook in Colab through this link². From there, we install Keras as follows:

install.packages("keras")