The Importance of Layers in a Neural Network

In an artificial neural network, nodes are arranged into layers. The layer that receives the data on which to provide inferences in a neural network is called the input layer. There can be intermediate layers as well, generally referred to as dense layers since they take inputs from other layers and then provide outputs to other layers. This is where things get more and more complicated because we don't have to necessarily connect all the neurons together. We can also loop things around, connect one layer to a previous layer.