feed-forward neural networks the number of units in layer is referred to as the width of the layer the width of each layer need not be the samebut the dimension should be alignedas we shall see later in the the number of layers is referred to as the depth of the network this is where the notion of "deep(as in "deep learning"comes from every layer takes as input the output produced by the previous layerexcept for the first layerwhich consumes the input the output of the last layer is the output of the network and is the prediction generated based on the input as previously mentioneda neural network can be seen as function fth ywhich takes as input rn and produces as output rmand the behavior of which is parameterized by th rp we can now be more precise about th it is simply collection of all the weights for all the units in the network designing neural network involvesamong other thingsdefining the overall structure of the networkincluding the number of layers (depthand the width of these layers figure - shows the overall structure of neural network