What is a FeedForward Neural Network?
In FeedForward Neural Network, the Perceptrons are arranged in layers, with the first Layer taking in inputs (Input Layer) and the last layer producing outputs(Output Layer). The middle layers have no connection with the external world and hence are called Hidden Layers.
Each perceptron in one layer is connected to every perceptron on the next layer. Thus the information is "fed forward" from one layer to the next. There is no connection among perceptrons in the same layer.
Since the information moves in only one direction i.e. forward, from the input nodes, through the hidden nodes (if any) and to the output nodes, the Network is named as Feed-Forward Neural Network.
Mathematical Representation of FeedForward Neural Network
FeedForward Neural Network with One Hidden Layer |
The above diagram denotes a simple Neural Network with one Input Layer nodes, go into another node (layer 2), which finally outputs the hypothesis function, known as the "output layer".
The intermediate or "hidden" layer nodes a02⋯an2 are called the "activation units."
ai(j) ->"activation" of unit i in layer j
θ(j) ->matrix of weights/parameters controlling function mapping from layer j to layer j+1
The values for each of the "activation" nodes is obtained as follows:
