Python for Deep Learning - Build Neural Networks in Python - Convolution Layer

The video tutorial explains convolution layers in neural networks, focusing on how filters are applied to input images to extract features. It uses a 6x6 image example with a 3x3 filter to demonstrate the process of generating feature maps. The tutorial covers the mathematical operations involved, including filter multiplication and summation, and explains the concept of stride and its impact on feature map size. It also discusses the role of activation functions in introducing nonlinearity to the network's output.

What is the primary purpose of applying a filter to an input image in a convolution layer?

• To increase the image size

• To change the color of the image

• To extract features from the image

• To convert the image to grayscale

In the process of generating a feature map, what operation is performed between the pixel values and the filter values?

• Addition

• Subtraction

• Multiplication

• Division

What is the term used to describe the movement of the filter across the image?

• Stride

• Slide

• Jump

• Shift

Why is the feature map smaller than the input image?

• Because the filter adds extra layers

• Because the filter cannot sit on the outermost values

• Because the filter removes noise

• Because the image is resized

What is the role of an activation function in a neural network?

• To linearly transform the input

• To introduce nonlinearity

• To decrease the network size

• To increase the number of nodes