Understanding AlexNet: The Deep Learning Revolution Explained in 10 Minutes

In 2011, I spent three sleepless nights tweaking SIFT features for a facial recognition project, only to hit a measly 60% accuracy. Then, in 2012, AlexNet burst onto the scene, winning the ImageNet challenge with a jaw dropping 15.3% error rate crushing the competition 26.2% using GPU

How? It learned its own filters, no hand tuning required. This guide breaks down AlexNet’s architecture, innovations, and why it’s still a cornerstone of AI.

Why AlexNet important:

Before AlexNet, computer vision was stuck. Hand engineered features like SIFT and HOG were slow, error prone, and couldn’t scale. AlexNet changed the game by:

• Using deep convolutional neural networks (CNNs) with 8 layers.