Every field has a moment where the story splits into "before" and "after." For deep learning, that moment has a year attached to it: 2012. This is the first post in a series where I'll be working through my Deep Learning course notes and turning them into something more digestible — starting at the very beginning, with the question of why this field exploded when it did.
The problem nobody could crack
Picture the state of computer vision before 2012. Researchers had a benchmark called ImageNet — a database of roughly 14 million images, organized into about 20,000 categories. A subset of this became the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), which asked systems to sort images into one of 1,000 classes, based on nothing but images scraped from the internet, each carrying a single label.
At the time, classifying images into a thousand categories wasn't just hard — it was considered close to impossible. Error rates on the challenge had been stuck around 25% (measured as "Top-5 error," meaning the correct label had to appear among a model's top five guesses) for years. Progress had stalled. Nobody had a clear path forward.
Enter AlexNet
