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What is back propagation? Explanation: Back propagation is the transmission of error back through the network to allow weights to be adjusted so that the network can learn.

## What is back propagation in neural network?

Back-propagation is just a way of propagating the total loss back into the neural network to know how much of the loss every node is responsible for, and subsequently updating the weights in such a way that minimizes the loss by giving the nodes with higher error rates lower weights and vice versa.

## Why is the XOR problem exceptionally interesting to neural network researchers?

Why is the XOR problem exceptionally interesting to neural network researchers? … Explanation: Linearly separable problems of interest of neural network researchers because they are the only class of problem that Perceptron can solve successfully.

## Which of the following is an application of neural network Mcq?

Assume that you are given a data set and a neural network model trained on the data set.

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Q. | Which of the following is an application of NN (Neural Network)? |
---|---|

A. | sales forecasting |

B. | data validation |

C. | risk management |

D. | all of the mentioned |

## What does BN means in NN Mcq?

Explanation: The full form BN is Bayesian networks and Bayesian networks are also called. Belief Networks or Bayes Nets.

## What is back propagation Javatpoint?

Backpropagation is one of the important concepts of a neural network. … Backpropagation algorithms are a set of methods used to efficiently train artificial neural networks following a gradient descent approach which exploits the chain rule.

## What is back propagation in machine learning?

Backpropagation, short for “backward propagation of errors,” is an algorithm for supervised learning of artificial neural networks using gradient descent. … Partial computations of the gradient from one layer are reused in the computation of the gradient for the previous layer.

## When the cell is said to be fired?

When the cell is said to be fired? Explanation: Cell is said to be fired if & only if potential of body reaches a certain steady threshold values.

## What is the objective of backpropagation algorithm?

Explanation: The objective of backpropagation algorithm is to to develop learning algorithm for multilayer feedforward neural network, so that network can be trained to capture the mapping implicitly.

## What is the complexity of Minirnax algorithm?

The time complexity of minimax is O(b^m) and the space complexity is O(bm), where b is the number of legal moves at each point and m is the maximum depth of the tree.

## What is the main advantage of backward state space search?

Explanation: The main advantage of backward search will allow us to consider only relevant actions. 7. What is the other name of the backward state-space search? Explanation: Backward state-space search will find the solution from goal to the action, So it is called as Regression planning.

## How many types of ANN are there?

This article focuses on three important types of neural networks that form the basis for most pre-trained models in deep learning: Artificial Neural Networks (ANN) Convolution Neural Networks (CNN) Recurrent Neural Networks (RNN)

## What is Perceptron * Mcq?

Explanation: The perceptron is a single layer feed-forward neural network. It is not an auto-associative network because it has no feedback and is not a multiple layer neural network because the pre-processing stage is not made of neurons. … The number of feedback paths(loops) does not have to be one.

## What is a back propagation Mcq?

Explanation: Back propagation is the transmission of error back through the network to allow weights to be adjusted so that the network can learn.

## What is BN in neural networks?

BN is the internal enforcer of normalization within the input values passed between the layer of a neural network. Internal normalization limits the covariate shift that usually occurs to the activations within the layers.

## What is the objective of Ann?

neural network, a computer program that operates in a manner inspired by the natural neural network in the brain. The objective of such artificial neural networks is to perform such cognitive functions as problem solving and machine learning.