As mentioned, Figure 3 shows a schematic of a feedforward, two-layered neural network. Given a set of input patterns (observations) with associated known outputs (responses), the objective is to train the network, using supervised learning, to estimate the functional relationship between the inputs and outputs. The networks can then be used to model or predict a response corresponding to a new input pattern. This is similar to the regression problem where we have a set of independent variables (inputs) and dependent variables (outputs), and we want to find relationship between the two.