Why CNNs exist

Images have structure:

• nearby pixels are related
• patterns repeat across the image

CNNs exploit this using convolutions.

Convolution intuition

A small filter (kernel) slides across the image to detect patterns.

  flowchart LR
  I[Image] --> K[Convolution kernel]
  K --> F[Feature map]

Key parts

• Convolution layers: learn filters
• Pooling: reduce spatial size, keep important features
• Fully connected layers: final classification/regression

Why they work well

• parameter sharing (same filter used across image)
• translation invariance (patterns recognized in different locations)

Mini-checkpoint

Why not use a plain MLP for images?

• too many parameters and ignores spatial structure.