Deep learning is the recent hot trend in machine learning/AI. It is all about building advanced neural networks. By making multiple hidden layers work in a neural network model, we can work with complex nonlinear representations of data. We create deep learning using base neural networks. Deep learning has numerous use cases in real life, such as, driverless cars, medical diagnostics, computer vision, speech recognition, Natural Language Processing (NLP), handwriting recognition, language translation, and many other fields.
In this chapter, we will deal with the deep learning process: how to train, test, and deploy a Deep Neural Network (DNN). We will look at the different packages available in R to handle DNNs. We will understand how to build and train a DNN with the neuralnet package. Finally, we will analyze an example of training...
