Deep Learning

Deep learning is how computers learn patterns too subtle to program by hand — turning pixels into objects, audio into words, and prompts into prose.

The basic unit is simple: multiply inputs by weights, add a bias, pass the result through a nonlinearity, and repeat. Stack enough of those and the whole network can approximate essentially any function.

Stacking layers is what adds power: the early ones learn edges and textures, the later ones objects and meaning — features composed from features, with no hand engineering.

Learning reduces to calculus. Backpropagation carries the error from the output back through every layer, computing for each weight the direction to adjust — and gradient descent takes the step.

Inside each cell, attention lets every token draw on every other; the mixed signal then passes through ordinary weighted units, and gradient descent tunes millions of those weights until the output matches the data.