MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -The perceptron is a mathematical model of a biological neuron. While in actual neurons the dendrite receives electrical signals from the axons of other neurons, in the perceptron these electrical signals are represented as numerical values.

Detailed explanation-2: -Perceptron is a single layer neural network and a multi-layer perceptron is called Neural Networks. Perceptron is a linear classifier (binary). Also, it is used in supervised learning. It helps to classify the given input data.

Detailed explanation-3: -Perceptron is a neural network with only one neuron, and can only understand linear relationships between the input and output data provided. However, with Multilayer Perceptron, horizons are expanded and now this neural network can have many layers of neurons, and ready to learn more complex patterns.

Detailed explanation-4: -A perceptron consists of four parts: input values, weights and a bias, a weighted sum, and activation function. The idea is simple, given the numerical value of the inputs and the weights, there is a function, inside the neuron, that will produce an output.

Detailed explanation-5: -A perceptron is a signal transmission network consisting of sensory units (S units), association units (A units), and output or response units (R units).