MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -The backpropagation algorithm performs learning on a multilayer feed-forward neural network. It iteratively learns a set of weights for prediction of the class label of tuples. A multilayer feed-forward neural network consists of an input layer, one or more hidden layers, and an output layer.

Detailed explanation-2: -MLP utilizes a supervised learning technique called backpropagation for training. Its multiple layers and non-linear activation distinguish MLP from a linear perceptron. It can distinguish data that is not linearly separable.

Detailed explanation-3: -The neurons in the MLP are trained with the back propagation learning algorithm. MLPs are designed to approximate any continuous function and can solve problems which are not linearly separable.

Detailed explanation-4: -Mutli-Layer Perceptron-Back Propagation. The Backpropagation neural network is a multilayered, feedforward neural network and is by far the most extensively used[6]. It is also considered one of the simplest and most general methods used for supervised training of multilayered neural networks[6].

Detailed explanation-5: -It enables the use of gradient methods, like gradient descent or stochastic gradient descent, to train multilayer networks and update weights to minimize loss.