17/04/2026
Before 2012, if you wanted a computer to recognize a "cat," humans had to manually program rules: "Look for two triangles for ears and whiskers." This was called Feature Engineering.
The problem? It was fragile. If the cat was upside down or blurry, the computer failed. The error rate was stuck around 25%—meaning the AI was wrong one out of every four times.
AlexNet changed the game by using a Convolutional Neural Network (CNN). Instead of humans telling the computer what a cat looks like, the researchers gave the network millions of images and let it figure out the patterns itself.
Convolutional Layers: These act like filters that scan the image. The first layers see simple things (edges and lines), while deeper layers see complex things (eyes, wheels, or ears).
Deep: It was "deep" because it had eight layers of these filters stacked on top of each other, which was massive for the time.
