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? Backpropagation Front Propagation Signal Propagation Channel Propagation None Hint 2). What do the gradients of backpropagation compute? Profit Function Loss Function Positive Function Negative Function None Hint Read More about Backpropagation Neural Network 3). Which rule is followed by the Backpropagation algorithm? Static Rule Dynamic Rule Chain Rule None None Hint 4). Neural networks training essence is _____________? Backpropagation Front Propagation Signal Propagation Channel Propagation None Hint 5). Error rates are reduced in backpropagation due to _____________? Proper Tuning Iteration Improper Tuning Generalization None Hint 6). How many layers are computed in the backpropagation algorithm at a single time? Four Three Two One None Hint 7). How the computation is generalized in the Backpropagation algorithm? Static Rule Dynamic Rule Delta Rule Sigma Rule None Hint 8). What doesn’t define how the gradient should be used? Backpropagation Front Propagation Signal Propagation Channel Propagation None Hint 9). Feedforward neural networks use ________________? Backpropagation Front Propagation Signal Propagation Channel Propagation None Hint 10). Which parameter should be set while using Backpropagation? Number of Inputs Number of Outputs Number of Gradients Number of Intermediate Stages None Hint 11). Neural networks are trained based on _______________ algorithm? Backpropagation Front Propagation Signal Propagation Channel Propagation None Hint 12). Backpropagation work with ______________neural networks? Single Layered Multi-layered Fixed Layered Dynamic Layered None Hint 13). How many layers does the backpropagation algorithm consist of? Zero Three Two One None Hint 14). Which is not the layer of the Backpropagation algorithm? Input Layer Hidden Layer Intermediate Layer Output Layer None Hint 15). Which layer in the backpropagation algorithm is utilized for adjusting weights? Input Layer Hidden Layer Intermediate Layer Output Layer None Hint 16). Differences among networks output and the probable outcome are calculated using _____________? Profit Function Loss Function Positive Function Negative Function None Hint 17). What are the various types of Backpropagation algorithms? Static Backpropagation Recurrent Backpropagation Dynamic Propagation a & b None Hint 18). Static inputs are mapped to static outcomes in ________________? Static Backpropagation Recurrent Backpropagation Dynamic Propagation a & b None Hint 19). Fixed point learning prefers _________________? Static Backpropagation Recurrent Backpropagation Dynamic Propagation a & b None Hint 20). Optical Character Recognition prefers ___________? Static Backpropagation Recurrent Backpropagation Dynamic Propagation a & b None Hint 21). Instant mapping is not offered in ____________? Static Backpropagation Recurrent Backpropagation Dynamic Propagation a & b None Hint 22). Weights in backpropagation algorithms are updated _____________? Forward Backward a & b None None Hint 23). Overhead in the Backpropagation algorithm is ________? More Less High Increased None Hint 24). Which algorithm is efficient in terms of memory? Backpropagation Forward Propagation Signal Propagation Channel Propagation None Hint 25). What are the layers in between the input and outcome layers of Neural networks? 0 Hidden Layers Present Either a or b Intermediate Layers None Hint Backpropagation Neural Network Algorithm MCQs for Quiz 26). Node in the neural networks providing more loss is adjusted by giving ___________? Large Weights Small Weights Maximum Weights None None Hint 27). Time complexity in Backpropagation algorithms is dependent upon ______________? Networks Structure Networks Gradient Network Loss Network Channel None Hint 28). Levenberg-Marquardt Backpropagation algorithm helps in adjusting _____________? Weight Variables Bias Variables Temperature Variables a & b None Hint 29). Backpropagation algorithm pseudocodes represent ______________? Top Language Description Plain Language Description Flat Language Description Bottom Language Description None Hint 30). Backpropagation algorithms are practically applied in _____________? Artificial Intelligence Natural Language Processing Image Processing All Mentioned Above None Hint 31). Backpropagation algorithm enables the usage of ____________? Gradient Methods Sigma Methods Pulse Methods Synchronous Methods None Hint 32). Special functions do not require to be learned in ______________? Backpropagation Front Propagation Signal Propagation Channel Propagation None Hint 33). Backpropagation algorithm used can calculate __________ quickly? Integration Derivation Addition Subtraction None Hint 34). Which algorithm is used in machine learning & data mining? Backpropagation Front Propagation Signal Propagation Channel Propagation None Hint 35). What happens to the Cost function when it meets the termination condition? Maximize Minimize Increase Enhance None Hint 36). What determines the influence of gradient in backpropagation? Number of Inputs Number of Outputs Learning Rate Number of Intermediate Stages None Hint 37). In backpropagation chain rule is followed to determine _____________? Number of Inputs Number of Outputs Gradients Number of Intermediate Stages None Hint 38). What is determined by the adjustment level of the Cost function? Number of Inputs Number of Outputs Gradients Number of Intermediate Stages None Hint 39). Backpropagation can minimize ___________? Profit Function Cost Function Positive Function Negative Function None Hint 40). How many neurons are in hidden layers of a Four-layer Neural network? Four Three Two One None Hint Backpropagation Neural Network Algorithm MCQs for Interviews 41). Four layers of neural networks have how many neurons in output layers? Four Three Two One None Hint 42). What adjusts the parameters of models in neural networks? Backpropagation Front Propagation Signal Propagation Channel Propagation None Hint 43). What happens if backpropagation is applied correctly? Reduced Error Rates Reliability Increased Increased Error Rates a & b None Hint 44). Backpropagation algorithm is highly sensitive for ________________? Noisy Data Data Irregularities a & b Noise Less Data None Hint 45). Backpropagation algorithms performance is dependent upon _______________? Output Data Intermediate Data Input Data All Mentioned Above None Hint 46). Matrix-based approach is preferable in _____________? Backpropagation Front Propagation Signal Propagation Channel Propagation None Hint 47). Which of these is easier to program? Backpropagation Front Propagation Signal Propagation Channel Propagation None Hint 48). What does not require prior information about Neural networks? Backpropagation Front Propagation Signal Propagation Channel Propagation None Hint 49). What is the standardized method that trains neural networks (artificial)? Backpropagation Front Propagation Signal Propagation Channel Propagation None Hint 50). Which algorithm is preferable in Data Mining? Backpropagation Front Propagation Signal Propagation Channel Propagation None Hint Please fill in the comment box below. Time's up