How CNNs Replace Millions of Weights

A dense layer wires every pixel to every neuron, with a separate weight for each connection, so on a real image you end up with millions of weights — and each one is glued to a single fixed pixel, which means an edge in the corner is invisible to the weights that only ever learned about the center. A convolution throws that out: you take one tiny three-by-three filter, just nine numbers, and slide it across the whole image, and at every stop you multiply the overlapping numbers and add them up to build a brand-new feature map. The magic is that it's the exact same nine weights at every position, so the filter that spots a vertical edge on the left is the very same filter that spots it anywhere else. And the arithmetic is worth getting right — minus two hundred times minus one is plus two hundred, because a negative times a negative is positive, so a dark edge under a negative weight lights up to plus four hundred instead of quietly cancelling out. Nine shared weights that find a pattern anywhere, instead of millions that each stare at one pixel — that's the whole trick. #shorts

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Видео How CNNs Replace Millions of Weights канала DataMListic