The output of the neural network depends on the input values, activation functions on each of the neurons, and weights on the connections. The goal is to find appropriate weights on each of the connections to accurately predict the output value. A correlation between inputs, weights, transfer, and activation functions can be visualized as follows:
Figure 4.4 ANN components correlation
In summary, within an ANN, we do the sum of products of input (X) to their weights (W) and apply the activation function f(x) to get the output of a layer that is passed as input to another layer. If ...
