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Backpropagation Neural Network Algorithm Question & Answers

November 12, 2022 By WatElectronics

This article lists 50 Backpropagation Neural Network Algorithm MCQs for engineering students. All the Backpropagation Neural Network Algorithm  Questions & Answers given below include a hint and a link wherever possible to the relevant topic. This is helpful for users who are preparing for their exams, interviews, or professionals who would like to brush up on the fundamentals of the Backpropagation Neural Network Algorithm. 

A group containing I/O units is connected in such a way that the weight of each connection is associated with its computer program known as a Neural Network (artificial). Predictive models are built from more extensive databases through artificial neural networks and this model is constructed based on Human Nervous System. It helps to understand images, speech Human learning, etc…

The neural network's essence is backpropagation. Error rates obtained from previous iterations make the neural network's weights get tuned finely. The properly tuned network can make the rates of error reduced and the model become more reliable by increased generalization.

Backward Propagation of Errors is nothing but Backpropagation in Neural networks. Artificial networks can be trained using such propagation. The loss function gradient is calculated using this method. Actual performance in this propagation of a particular problem depends upon the applied data.

The backpropagation algorithm is categorized into two types. They are: Static and Recurrent. A network that produces static outcomes for applied static inputs is referred to as Static backpropagation. To achieve fixed values recurrent back-propagation is preferred.

1). ______________ algorithm that propagates errors from nodes of output to input?

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2). What do the gradients of backpropagation compute?

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3). Which rule is followed by the Backpropagation algorithm?

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4). Neural networks training essence is _____________?

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5). Error rates are reduced in backpropagation due to _____________?

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6). How many layers are computed in the backpropagation algorithm at a single time?

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7). How the computation is generalized in the Backpropagation algorithm?

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8). What doesn’t define how the gradient should be used?

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9). Feedforward neural networks use ________________?

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10). Which parameter should be set while using Backpropagation?

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11). Neural networks are trained based on _______________ algorithm?

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12). Backpropagation work with ______________neural networks?

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13). How many layers does the backpropagation algorithm consist of?

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14). Which is not the layer of the Backpropagation algorithm?

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15). Which layer in the backpropagation algorithm is utilized for adjusting weights?

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16). Differences among networks output and the probable outcome are calculated using _____________?

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17). What are the various types of Backpropagation algorithms?

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18). Static inputs are mapped to static outcomes in ________________?

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19). Fixed point learning prefers _________________?

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20). Optical Character Recognition prefers ___________?

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21). Instant mapping is not offered in ____________?

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22). Weights in backpropagation algorithms are updated _____________?

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23). Overhead in the Backpropagation algorithm is ________?

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24). Which algorithm is efficient in terms of memory?

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25). What are the layers in between the input and outcome layers of Neural networks?

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Backpropagation Neural Network Algorithm MCQs for Quiz

26). Node in the neural networks providing more loss is adjusted by giving ___________?

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27). Time complexity in Backpropagation algorithms is dependent upon ______________?

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28). Levenberg-Marquardt Backpropagation algorithm helps in adjusting _____________?

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29). Backpropagation algorithm pseudocodes represent ______________?

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30). Backpropagation algorithms are practically applied in _____________?

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31). Backpropagation algorithm enables the usage of ____________?

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32). Special functions do not require to be learned in ______________?

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33). Backpropagation algorithm used can calculate __________ quickly?

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34). Which algorithm is used in machine learning & data mining?

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35). What happens to the Cost function when it meets the termination condition?

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36). What determines the influence of gradient in backpropagation?

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37). In backpropagation chain rule is followed to determine _____________?

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38). What is determined by the adjustment level of the Cost function?

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39). Backpropagation can minimize ___________?

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40). How many neurons are in hidden layers of a Four-layer Neural network?

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Backpropagation Neural Network Algorithm MCQs for Interviews

41). Four layers of neural networks have how many neurons in output layers?

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42). What adjusts the parameters of models in neural networks?

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43). What happens if backpropagation is applied correctly?

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44). Backpropagation algorithm is highly sensitive for ________________?

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45). Backpropagation algorithms performance is dependent upon _______________?

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46). Matrix-based approach is preferable in _____________?

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47). Which of these is easier to program?

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48). What does not require prior information about Neural networks?

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49). What is the standardized method that trains neural networks (artificial)?

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50). Which algorithm is preferable in Data Mining?

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