Neural Networks: From Perceptrons to Deep Learning

You’ve taken the elevator to the ninth floor. The doors open to what looks like a neuroscience laboratory crossed with a vintage computing museum. On one wall, a 1958 photograph shows Frank Rosenblatt standing proudly beside the Mark I Perceptron—a room-sized machine with 400 photocells and adjustable weights. On another, a timeline traces the “AI winter” of the 1970s, when funding dried up and dreams were deferred. And on the third wall, a modern GPU cluster hums quietly, running the very algorithms that were dismissed as impossible decades ago.