
Convolutional Neural Network Quiz
Quiz
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Computers
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University
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Practice Problem
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Medium
M Kanipriya
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10 questions
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1.
MULTIPLE CHOICE QUESTION
45 sec • 1 pt
What is a convolutional neural network (CNN) and how is it different from a regular neural network?
CNN is designed for processing text data, while regular neural network is designed for processing visual data.
CNN uses convolutional layers to process visual data, while regular neural network does not.
CNN has fewer parameters than regular neural network, making it less powerful.
CNN uses only fully connected layers, while regular neural network uses convolutional layers.
2.
MULTIPLE CHOICE QUESTION
45 sec • 1 pt
Explain the concept of convolution in the context of CNNs.
Applying a filter to an input to produce a feature map
Multiplying two matrices together
Adding all the elements of a matrix
Finding the maximum value in a matrix
3.
MULTIPLE CHOICE QUESTION
45 sec • 1 pt
What is the purpose of using pooling layers in a CNN?
To add noise to the data and reduce model performance
To increase spatial dimensions and encourage overfitting
To make the model more complex and difficult to train
To reduce spatial dimensions and control overfitting
4.
MULTIPLE CHOICE QUESTION
45 sec • 1 pt
Describe the role of activation functions in a CNN.
Activation functions introduce non-linearity into the network
Activation functions are only used in fully connected layers of the CNN
Activation functions are used to initialize the weights of the CNN
Activation functions help in reducing the dimensionality of the input data
5.
MULTIPLE CHOICE QUESTION
45 sec • 1 pt
What are the advantages of using CNNs for image recognition tasks?
CNNs can automatically learn features from data and are well-suited for capturing spatial hierarchies in images.
CNNs require a large amount of labeled data
CNNs are not suitable for image recognition tasks
CNNs are not capable of capturing spatial hierarchies in images
6.
MULTIPLE CHOICE QUESTION
45 sec • 1 pt
Explain the concept of feature maps in the context of CNNs.
The size of the input image
The color palette used in an image
The output of applying a filter to an input image
The number of layers in a CNN
7.
MULTIPLE CHOICE QUESTION
45 sec • 1 pt
What is the role of padding in CNNs and why is it used?
It is used to preserve the spatial dimensions of the input as it passes through the layers of the network.
It is used to reduce the spatial dimensions of the input
It is used to increase the computational complexity of the network
It is used to add noise to the input data
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