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

The video tutorial explains the concept of pooling in image processing, focusing on reducing the dimensions of feature maps to decrease the number of parameters to learn. It introduces Max pooling and Average pooling, highlighting their differences. Max pooling extracts the maximum value from a region, preserving bright pixels, while Average pooling smooths the image, potentially losing sharp features. An example of Max pooling with a 2x2 size and stride of 2 is demonstrated, showing how it reduces the feature map's dimensions.

What is the purpose of pooling in image processing?

Explain the difference between Max pooling and average pooling.

How does Max pooling affect the features of an image?

In what scenarios is Max pooling particularly useful?

What happens to the dimensions of the feature map after applying pooling?