Summary

Deep learning is a subject of importance right from image detection to speech recognition and AI-related activity. There are numerous products and packages in the market for deep learning. Some of these are Keras, TensorFlow, h2o, and many others.

In this chapter, we learned the basics of deep learning, many variations of DNNs, the most important deep learning algorithms, and the basic workflow for deep learning. We explored the different packages available in R to handle DNNs.

To understand how to build and train a DNN, we analyzed a practical example of DNN implementation with the neuralnet package. We learned how to normalize data across the various available techniques, to remove data units, allowing you to easily compare data from different locations. We saw how to split the data for the training and testing of the network. We learned to use the neuralnet function to build and train a multilayered neural network. So we understood how to use the trained network to make predictions...