In this episode, we unpack why deep learning suddenly succeeded after decades of limited progress. Although neural networks were invented in the 1940s and refined through the perceptron era, connectionism stalled due to shallow architectures, linear separability limits, scarce data, and insufficient compute. The modern breakthrough emerged only when three factors finally converged: better algorithms, abundant data, and powerful GPU-based computation.

We trace this journey from the early perceptron failures and the rise of SVMs, to the shift toward representation learning—where deep networks learn hierarchical features directly from raw data. With stable training made possible by backpropagation refinements, ReLU activations, improved initialization, and layerwise pretraining, deep models became practical just as massive datasets like ImageN ...