Fundamentals of Neural Networks - Convolution in 2D and 3D

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Computers
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11th - 12th Grade
•
Hard
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7 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What are the key parameters discussed for designing deep CNNs?
Dropout rate and regularization
Batch size and learning rate
Activation functions and loss functions
Matrix tensors, padding, and stride
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
In a 2D convolution operation, what is the role of the filter?
To slide over the image and perform element-wise multiplication
To convert the image to grayscale
To add noise to the image
To enhance the image resolution
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How does a 3D convolution differ from a 2D convolution?
It is only used for video processing
It involves multiple channels for color images
It uses a larger filter size
It requires more computational power
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the purpose of using a 3-channel filter in 3D convolution?
To convert the image to black and white
To reduce the image size
To match the RGB channels of a color image
To increase the image brightness
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the main function of pooling operations in CNNs?
To increase the number of channels
To reduce the dimensionality of the feature map
To enhance the color contrast
To add more layers to the network
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which pooling method can be used to summarize the output of a convolutional layer?
Max pooling
Min pooling
Gradient pooling
Sum pooling
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is a common application of 3D convolution mentioned in the lecture?
Speech recognition
Weather forecasting
MRI image analysis
Facial recognition
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