CNN Layers Explained: Convolution, Pooling, Activation, BatchNorm
CNN Layers Explained: Convolution, Pooling, Activation, BatchNorm
Convolution
Detects local patterns; learned filters become features.
Pooling
Downsamples to reduce compute and add robustness.
BatchNorm
Stabilizes training and improves gradient behavior.
