Deep Learning

Deep learning is what happens when you make neural networks much deeper - adding many more layers between input and output. The "deep" simply refers to depth: lots of layers stacked on top of each other. This matters because each layer can learn to recognise increasingly abstract features. In an image recognition system, early layers might detect edges, middle layers might recognise shapes, and deep layers might identify faces or objects. Before about 2012, most neural networks were shallow because deeper ones were extremely difficult to train - they'd either learn too slowly or become unstable. Breakthroughs in training techniques, combined with powerful graphics cards (GPUs) and vast amounts of data, suddenly made deep networks practical. The results were dramatic: deep learning shattered records in image recognition, speech processing and translation almost overnight. Today, when people say "AI," they often mean deep learning. The important thing to understand is that deep learning's power comes from scale - more layers, more data, more computing power - and that this relationship between scale and performance has driven most of the AI progress you've seen in recent years.