Neural Networks QUIZ (MCQ QUESTIONS AND ANSWERS)

Total Correct: 0

Time:20:00

Question: 1

Which of the following is a common method for reducing the dimensionality of input data in a neural network?

Principal Component Analysis (PCA)

Gradient Descent

Backpropagation

Weight Initialization

Question: 2

In a neural network, what is the term used to describe a complete pass through the training data during the training process?

Epoch

Iteration

Batch

Step

Question: 3

Which of the following is a technique for increasing the amount of training data in a neural network?

Data augmentation

Feature extraction

One-hot encoding

Batch normalization

Question: 4

What is the purpose of weight initialization in a neural network?

To set initial values for weights and biases

To update weights during training

To introduce non-linearity

To compute the gradient

Question: 5

Which of the following is not a type of layer in a neural network?

Input layer

Hidden layer

Output layer

Support layer

Question: 6

In a Convolutional Neural Network (CNN), what is the purpose of a filter or kernel?

To apply a local operation on the input data

To introduce non-linearity

To update weights

To reduce the dimensionality of the input data

Question: 7

What is the process of converting input data into a fixed-size vector before feeding it into a neural network called?

Feature extraction

Feature scaling

One-hot encoding

Data augmentation

Question: 8

Which of the following is a technique for improving the performance of a neural network ensemble?

Bagging

Regularization

Feature scaling

Weight initialization

Question: 9

What is the purpose of the Cross-Entropy Loss function in a neural network?

To measure the difference between two probability distributions

To introduce non-linearity

To update weights

To compute the gradient

Question: 10

What is the purpose of the Softmax activation function?

To convert input values into probabilities

To introduce non-linearity

To update weights

To reduce the dimensionality of the input data

Question: 11

What is the term used to describe the ability of a neural network to generalize well to new, unseen data?

Overfitting

Regularization

Generalization

Convergence

Question: 12

In a Recurrent Neural Network (RNN), what is the purpose of the hidden state?

To store information from previous time steps

To update weights

To reduce the dimensionality of the input data

To introduce non-linearity

Question: 13

What is the purpose of the pooling layer in a Convolutional Neural Network (CNN)?

To reduce the spatial dimensions of the feature maps

To apply non-linearity

To learn features from input data

To update weights

Question: 14

Which of the following is a common method for preventing vanishing gradient problem in deep neural networks?

Using ReLU activation function

Using a smaller learning rate

Using more hidden layers

Using dropout regularization

Question: 15

What is the purpose of batch normalization in a neural network?

To speed up training

To prevent overfitting

To introduce non-linearity

To improve generalization

Question: 16

What is the basic building block of a neural network?

Node

Neuron

Layer

Weight

Question: 17

What is the process of fine-tuning hyperparameters to optimize a neural network's performance called?

Regularization

Hyperparameter tuning

Gradient descent

Feature scaling

Question: 18

Which of the following is not a hyperparameter in a neural network?

Learning rate

Number of hidden layers

Activation function

Input data

Question: 19

What is the term used to describe a neural network that has multiple hidden layers?

Shallow Neural Network

Deep Neural Network

Convolutional Neural Network

Recurrent Neural Network

Question: 20

What is the purpose of the learning rate in a neural network?

To control the speed of weight updates

To determine the number of layers

To set the activation function

To initialize the weights

Question: 21

Which of the following optimizers is most commonly used in neural networks?

Stochastic Gradient Descent (SGD)

Nesterov Accelerated Gradient (NAG)

AdaBoost

Naive Bayes

Question: 22

What is the role of the optimizer in a neural network?

To select the best activation function

To minimize the loss function

To determine the number of layers

To set the learning rate

Question: 23

What is the purpose of the loss function in a neural network?

To measure the accuracy of the model

To update the weights

To compute the gradient

To measure the difference between predicted output and actual output

Question: 24

What is the primary difference between a Long Short-Term Memory (LSTM) network and a standard Recurrent Neural Network (RNN)?

LSTMs have a more complex structure

LSTMs are better at handling long-term dependencies

LSTMs are faster to train

LSTMs require more training data

Question: 25

Which type of neural network is designed to handle sequence data and has a memory of previous inputs?

Convolutional Neural Network (CNN)

Recurrent Neural Network (RNN)

Deep Neural Network (DNN)

Feedforward Neural Network (FNN)

Question: 26

In which type of neural network are layers arranged in a hierarchical order, with each layer learning to recognize increasingly complex features?

Convolutional Neural Network (CNN)

Recurrent Neural Network (RNN)

Deep Neural Network (DNN)

Long Short-Term Memory (LSTM)

Question: 27

What is the process of updating weights in a neural network called?

Forward propagation

Backpropagation

Gradient descent

Learning rate

Question: 28

Which of the following is a technique used to prevent overfitting in neural networks?

Normalization

Dropout

Activation function

Backpropagation

Question: 29

Which of the following is not a common activation function?

Sigmoid

ReLU

Tanh

Euclidean

Question: 30

What is the purpose of the activation function in a neural network?

To introduce non-linearity into the model

To perform matrix multiplication

To compute loss

To update weights

Neural Networks QUIZ (MCQ QUESTIONS AND ANSWERS)

TIME:20:00

Time:20:00

Which of the following is a common method for reducing the dimensionality of input data in a neural network?

In a neural network, what is the term used to describe a complete pass through the training data during the training process?

Which of the following is a technique for increasing the amount of training data in a neural network?

What is the purpose of weight initialization in a neural network?

Which of the following is not a type of layer in a neural network?

In a Convolutional Neural Network (CNN), what is the purpose of a filter or kernel?

What is the process of converting input data into a fixed-size vector before feeding it into a neural network called?

Which of the following is a technique for improving the performance of a neural network ensemble?

What is the purpose of the Cross-Entropy Loss function in a neural network?

What is the purpose of the Softmax activation function?

What is the term used to describe the ability of a neural network to generalize well to new, unseen data?

In a Recurrent Neural Network (RNN), what is the purpose of the hidden state?

What is the purpose of the pooling layer in a Convolutional Neural Network (CNN)?

Which of the following is a common method for preventing vanishing gradient problem in deep neural networks?

What is the purpose of batch normalization in a neural network?

What is the basic building block of a neural network?

What is the process of fine-tuning hyperparameters to optimize a neural network's performance called?

Which of the following is not a hyperparameter in a neural network?

What is the term used to describe a neural network that has multiple hidden layers?

What is the purpose of the learning rate in a neural network?

Which of the following optimizers is most commonly used in neural networks?

What is the role of the optimizer in a neural network?

What is the purpose of the loss function in a neural network?

What is the primary difference between a Long Short-Term Memory (LSTM) network and a standard Recurrent Neural Network (RNN)?

Which type of neural network is designed to handle sequence data and has a memory of previous inputs?

In which type of neural network are layers arranged in a hierarchical order, with each layer learning to recognize increasingly complex features?

What is the process of updating weights in a neural network called?

Which of the following is a technique used to prevent overfitting in neural networks?

Which of the following is not a common activation function?

What is the purpose of the activation function in a neural network?