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.

Explain the significance of the activation function in the context of feature maps.

How does the size of the filter affect the output feature map?