MACHINE LEARNING

Detailed explanation-1: -During this learning phase, the network learns by adjusting the weights so as to be able to predict the correct class label of input samples. Neural network learning is also referred to as “connectionist learning, ‘’ due to connections between the units.

Detailed explanation-2: -Weight is the parameter within a neural network that transforms input data within the network’s hidden layers. A neural network is a series of nodes, or neurons. Within each node is a set of inputs, weight, and a bias value.

Detailed explanation-3: -Widrow-Hoff Learning (Delta Learning Rule) The weights are adjusted according to the weighted sum of the inputs (the net), whereas in perceptron the sign of the weighted sum was useful for determining the output as the threshold was set to 0, -1, or +1. This makes ADALINE different from the normal perceptron.