Convolutional Neural Networks Quiz

What is the purpose of feature extraction in Convolutional Neural Networks (CNN)?

• To identify and extract important patterns or features from the input data

• To add noise to the input data

• To decrease the complexity of the model

• To increase the training time

Which of the following is not a type of pooling layer used in CNN?

• Fully-connected pooling

• Average pooling

• Global pooling

• Max pooling

What is the main function of convolutional layers in CNN?

• Extracting features from the input data

• Adding noise to the input data

• Reshaping the input data

• Blurring the input data

What is the purpose of CNN architecture and design?

• To cook food

• To play music

• To extract features from input images and classify them into different categories

• To write novels

What is the main goal of training and optimization of CNN?

• Decrease the accuracy and efficiency of the model

• Increase the complexity of the model without improving accuracy

• Have no impact on the model's performance

• Improve the accuracy and efficiency of the model

Which layer in CNN is responsible for reducing the spatial dimensions of the input?

• Pooling layer

• Activation layer

• Fully connected layer

• Convolutional layer

What is the purpose of using activation functions in CNN?

• To make the network slower and less efficient

• To reduce the accuracy of the network

• To make the network more linear and less capable of learning complex patterns

• To introduce non-linearity and allow the network to learn more complex patterns

What is the role of padding in convolutional layers of CNN?

• To reduce the spatial dimensions of the image

• To preserve spatial dimensions and prevent loss of information at the edges of the image.

• To introduce noise into the convolutional layers

• To increase the computational complexity of the model

What is the purpose of using dropout in CNN?

• To increase the model complexity

• To prevent overfitting

• To speed up the training process

• To improve the accuracy of the model