In September 2012 AlexNet won the ImageNet Large Scale Visual Recognition Challenge with an error margin 10% lower than the second-place classifier — an unprecedented gap in the competition's history. The network used GPUs for training, a deep convolutional architecture, and dropout for regularization. The result was a turning point: it proved to the entire AI community that deep neural networks, trained on large data with parallel hardware, could radically outperform classical approaches. Within three years, practically all computer-vision research had migrated to deep learning. AlexNet did not invent deep learning — that credit belongs to decades of work by Hinton, LeCun, Bengio, and others — but it demonstrated it irrefutably to the world.