MACHINE LEARNING

Detailed explanation-1: -2. What was the 2nd stage in perceptron model called? Explanation: This was the very speciality of the perceptron model, that is performs association mapping on outputs of he sensory units.

Detailed explanation-2: -It consists of three types of layers-the input layer, output layer and hidden layer, as shown in Fig. 3. The input layer receives the input signal to be processed. The required task such as prediction and classification is performed by the output layer.

Detailed explanation-3: -The perceptron model begins with multiplying all input values and their weights, then adds these values to create the weighted sum. Further, this weighted sum is applied to the activation function ‘f’ to obtain the desired output. This activation function is also known as the step function and is represented by ‘f. ‘

Detailed explanation-4: -Single layer perceptrons-These can only learn linearly separable patterns. Multilayer perceptrons-These have the greatest processing power. They are a class of feedforward neural networks. They have a hidden layer and use sophisticated algorithms like backpropagation.