ML//CNN//convolution

A convolution applies the same learned kernel at every spatial position, producing a local weighted sum. Small kernels can compose edges and textures into progressively larger receptive fields.

A convolution applies the same learned kernel at every spatial position, producing a local weighted sum. Small kernels can compose edges and textures into progressively larger receptive fields.

Weight sharing makes the operation translation equivariant: shifting the input shifts the feature map. Invariance requires an additional mechanism such as pooling, aggregation, or training augmentation.

The kernel encodes locality as an architectural prior. This makes CNNs data-efficient for images, but less natural when every location must interact globally from the first layer.

Padding, stride, and boundary handling are not implementation trivia; they determine which spatial information survives and how coordinates leak into the representation.