Thinking through my experience in working with Deep learning models has been rewarding. From reading raw pixels to powering self-driving cars, CNNs remain the cornerstone of modern visual perception. This article walks through how they work, why they matter, and where they’re headed.
Why Convolution?
Convolution, in a nutshell, is a way of “mixing” two functions (or two arrays of numbers) so that one acts as a filter over the other. It measures how much the two overlap as one slides (shifts) across the other. Because of that sliding‑and‑multiplying behavior, convolution extracts local patterns and produces a new signal or image in which those patterns are emphasized or suppressed.