Frequent question: What is back propagation in neural network Mcq?

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.

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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.