Deep Learning with R Cookbook

How it works...

In step 1, we loaded the MNIST dataset. The x data was a 3D array of grayscale values of the form (images, width, height). In step 2, we flattened these 28x28 images into a vector of length 784. Then, we normalized the grayscale values between 0 and 1. In step 3, we one-hot encoded the target variable using the to_categorical() function from keras to convert this into a binary format matrix.

In step 4, we built a sequential model by stacking dense and dropout layers. In a dense layer, every neuron receives input from all the neurons of the previous layer, which is why it's known as being densely connected. In our model, each layer took input from the previous layer and applied an activation to the output of our previous layer. ...