The mxnet library offers several functions that enable us to define the layers and activations that comprise the deep learning network. The definition of layers, the usage of activation functions, and the number of neurons to be used in each of the hidden layers is generally termed the network architecture. Deciding on the network architecture is more of an art than a science. Often, several iterations of experiments may be needed to decide on the right architecture for the problem. We call it an art as there are no exact rules for finding the ideal architecture. The number of layers, neurons in these layers, and the type of layers are pretty much decided through trial and error.
In this section, we'll build a simple deep learning network with three hidden layers. Here is the general architecture of our...
