In Fig.14.1a, the rst layer contains the input nodes, which receive the initial data from outside sources supplied by a human being in the form of images, texts, etc. Each node in the input layer is assigned numerical values N1, N2, N3, etc., along with corresponding weights W1, W2, W3, etc. Weights in ANN work like knobs that the network tunes to t the data, and are used to determine the importance of an input to the output. All the weighted node values are summed up along with a bias value B to give a single net input value C as given by Eq.14.1. This result is then passed through an activation function f to produce an output The bias B is an addi­tional parameter in a node that allows the model more exibility to shift the activa­tion function for a better t of the data by moving it up or down. The role of the activation function is to introduce nonlinearities in the network, required to adjust nonlinear relationships between input and output. A common activation function is the rectied linear unit (ReLU), which simply sets negative output values to zero. Other popular activation functions are the sigmoid, hyperbolic tangent and leaky ReLU.