Neural Network Algorithms MCQs
1. Which activation function is commonly used in the hidden layers of a neural network to introduce non-linearity?
a. Sigmoid
b. ReLU (Rectified Linear Unit)
c. Tanh (Hyperbolic Tangent)
d. Linear
Answer: b. ReLU (Rectified Linear Unit)
2. What is the purpose of the activation function in a neural network?
a. It determines the number of layers in the network
b. It normalizes the input data
c. It introduces non-linearity in the network
d. It controls the learning rate of the network
Answer: c. It introduces non-linearity in the network
3. Which neural network architecture is used for handling sequential data, such as natural language processing or time series analysis?
a. Feedforward Neural Network (FNN)
b. Convolutional Neural Network (CNN)
c. Recurrent Neural Network (RNN)
d. Radial Basis Function Network (RBFN)
Answer: c. Recurrent Neural Network (RNN)
4. Which neural network architecture is commonly used for image classification tasks?
a. Feedforward Neural Network (FNN)
b. Convolutional Neural Network (CNN)
c. Recurrent Neural Network (RNN)
d. Radial Basis Function Network (RBFN)
Answer: b. Convolutional Neural Network (CNN)
5. Which algorithm is used for updating the weights in a neural network during the training process?
a. Backpropagation
b. Gradient Descent
c. Stochastic Gradient Descent (SGD)
d. All of the above
Answer: d. All of the above
6. What is the purpose of the bias term in a neural network?
a. It controls the learning rate of the network
b. It adds flexibility to the decision boundaries of the network
c. It introduces non-linearity in the network
d. It allows shifting the activation function
Answer: d. It allows shifting the activation function
7. Which algorithm is used for updating the weights in a neural network with a single training example at a time?
a. Backpropagation
b. Gradient Descent
c. Stochastic Gradient Descent (SGD)
d. Mini-batch Gradient Descent
Answer: c. Stochastic Gradient Descent (SGD)
8. Which technique is used for preventing overfitting in a neural network by randomly dropping out neurons during training?
a. Dropout
b. Batch Normalization
c. L1 Regularization
d. L2 Regularization
Answer: a. Dropout
9. What is the purpose of the loss function in a neural network?
a. It measures the accuracy of predictions
b. It measures the complexity of the model
c. It quantifies the difference between predicted and actual values
d. It controls the learning rate of the network
Answer: c. It quantifies the difference between predicted and actual values
10. Which algorithm is used for updating the weights in a neural network by considering the previous weight update?
a. Backpropagation through time (BPTT)
b. Resilient Propagation (RProp)
c. Levenberg-Marquardt Algorithm
d. Quickprop
Answer: b. Resilient Propagation (RProp)
11. Which neural network architecture is used for handling both sequential and spatial data, such as video processing or
3D image analysis?
a. Feedforward Neural Network (FNN)
b. Convolutional Neural Network (CNN)
c. Recurrent Neural Network (RNN)
d. Long Short-Term Memory (LSTM) Network
Answer: d. Long Short-Term Memory (LSTM) Network
12. Which algorithm is used for updating the weights in a neural network by considering the direction of steepest descent?
a. Backpropagation
b. Gradient Descent
c. Conjugate Gradient
d. Newton's Method
Answer: c. Conjugate Gradient
13. What is the purpose of the learning rate in a neural network?
a. It controls the speed of convergence during training
b. It determines the number of hidden layers in the network
c. It introduces non-linearity in the network
d. It allows shifting the activation function
Answer: a. It controls the speed of convergence during training
14. Which algorithm is used for updating the weights in a neural network by considering the direction of the negative gradient?
a. Backpropagation
b. Gradient Descent
c. Adam Optimization
d. Adaboost
Answer: b. Gradient Descent
15. Which neural network architecture is used for handling variable-length sequential data, such as text generation or machine translation?
a. Feedforward Neural Network (FNN)
b. Convolutional Neural Network (CNN)
c. Recurrent Neural Network (RNN)
d. Transformer Network
Answer: d. Transformer Network
16. Which technique is used for normalizing the input data in a neural network to ensure similar scales across different features?
a. Dropout
b. Batch Normalization
c. L1 Regularization
d. L2 Regularization
Answer: b. Batch Normalization
17. Which algorithm is used for updating the weights in a neural network by considering the direction of the negative gradient and the magnitude of the previous weight update?
a. Backpropagation through time (BPTT)
b. Resilient Propagation (RProp)
c. Levenberg-Marquardt Algorithm
d. Quickprop
Answer: d. Quickprop
18. Which neural network architecture is used for handling both sequential and hierarchical data, such as natural language parsing or speech recognition?
a. Feedforward Neural Network (FNN)
b. Convolutional Neural Network (CNN)
c. Recursive Neural Network (ReNN)
d. Radial Basis Function Network (RBFN)
Answer: c. Recursive Neural Network (ReNN)
19. Which technique is used for preventing overfitting in a neural network by adding a penalty term to the loss function based on the weights?
a. Dropout
b. Batch Normalization
c. L1 Regularization
d. L2 Regularization
Answer: d. L2 Regularization
20. Which algorithm is used for updating the weights in a neural network by considering the direction of the negative gradient and the Hessian matrix?
a. Backpropagation
b. Gradient Descent
c. Conjugate Gradient
d. Newton's Method
Answer: d. Newton's Method
21. Which neural network architecture is used for handling both sequential and non-sequential data, such as sentiment analysis or document classification?
a. Feedforward Neural Network (FNN)
b. Convolutional Neural Network (CNN)
c. Recurrent Neural Network (RNN)
d. Transformer Network
Answer: a. Feedforward
Neural Network (FNN)
22. Which algorithm is used for updating the weights in a neural network by considering the direction of the negative gradient and the momentum term?
a. Backpropagation
b. Gradient Descent
c. Stochastic Gradient Descent (SGD)
d. Momentum-based Gradient Descent
Answer: d. Momentum-based Gradient Descent
23. What is the purpose of the momentum term in a neural network?
a. It controls the speed of convergence during training
b. It introduces non-linearity in the network
c. It allows shifting the activation function
d. It helps accelerate the convergence and overcome local minima
Answer: d. It helps accelerate the convergence and overcome local minima
24. Which technique is used for preventing overfitting in a neural network by randomly selecting a subset of the training examples for each iteration?
a. Dropout
b. Batch Normalization
c. L1 Regularization
d. Mini-batch Gradient Descent
Answer: d. Mini-batch Gradient Descent
25. Which algorithm is used for updating the weights in a neural network by considering the direction of the negative gradient and adapting the learning rate for each weight?
a. Backpropagation
b. Gradient Descent
c. Adam Optimization
d. Adaboost
Answer: c. Adam Optimization
26. What is the purpose of the early stopping technique in a neural network?
a. It prevents the network from overfitting the training data
b. It speeds up the convergence of the network
c. It allows shifting the activation function
d. It controls the learning rate of the network
Answer: a. It prevents the network from overfitting the training data
27. Which neural network architecture is used for handling both sequential and spatial data, such as video processing or 3D image analysis?
a. Feedforward Neural Network (FNN)
b. Convolutional Neural Network (CNN)
c. Recurrent Neural Network (RNN)
d. Long Short-Term Memory (LSTM) Network
Answer: d. Long Short-Term Memory (LSTM) Network
28. Which algorithm is used for updating the weights in a neural network by considering the direction of the negative gradient and the magnitude of the previous weight update, with adaptive learning rates for each weight?
a. Backpropagation through time (BPTT)
b. Resilient Propagation (RProp)
c. Levenberg-Marquardt Algorithm
d. Adam Optimization
Answer: d. Adam Optimization
29. What is the purpose of the dropout technique in a neural network?
a. It prevents the network from overfitting the training data
b. It speeds up the convergence of the network
c. It allows shifting the activation function
d. It introduces non-linearity in the network
Answer: a. It prevents the network from overfitting the training data
30. Which algorithm is used for updating the weights in a neural network by considering the direction of the negative gradient and the second derivative (Hessian matrix)?
a. Backpropagation
b. Gradient Descent
c. Conjugate Gradient
d. Newton's Method
Answer: d. Newton's Method