Until now, we have looked at model architectures where the neural network has only one hidden layer between the input and the output layers. In this section, we will look at the neural network where there are multiple hidden layers (and hence a deep neural network), while reusing the same MNIST training and test dataset that were scaled.
Building a deep neural network to improve network accuracy
Getting ready
A deep neural network means that there are multiple hidden layers connecting the input to the output layer. Multiple hidden layers ensure that the neural network learns a complex non-linear relation between the input and output, which a simple neural network cannot learn (due to a limited number of hidden layers).
A typical...
