Neural Network Convolutional Neural Networks

Question 1

What is a convolutional layer and what operation does it perform?

Accepted Answer

A layer that slides learned filters across the input to produce feature maps

Answer

A convolutional layer applies learnable filters (kernels) across the spatial dimensions of the input, producing feature maps that detect local patterns.

Question 2

What does max pooling accomplish in a CNN?

Accepted Answer

It downsamples feature maps by taking the maximum value in each pooling window

Answer

Max pooling reduces the spatial dimensions of feature maps by selecting the maximum activation within each pooling window, providing spatial invariance.

Question 3

What is the receptive field of a neuron in a CNN?

Accepted Answer

The region of the input image that influences that neuron's activation

Answer

The receptive field is the spatial region of the original input that a given neuron in a deeper layer 'sees' and is influenced by.

Question 4

What is the purpose of padding in convolutional layers?

Accepted Answer

To preserve spatial dimensions and allow filters to be applied at border regions

Answer

Padding (typically zero-padding) adds border pixels to the input so that the output feature map maintains the same spatial dimensions and edge information is not lost.

Question 5

What does the stride parameter control in a convolutional layer?

Accepted Answer

The step size with which the filter slides across the input

Answer

Stride controls how many pixels the convolutional filter moves at each step; a stride of 2 halves the spatial dimensions of the output compared to stride 1.

Neural Network Practice Test

Neural Network Practice Test

Neural Network Convolutional Neural Networks