Neural Network

A neural network takes numbers in, passes them through layers of units that each combine their inputs using learned weights, and produces numbers out. Training works by showing the network a batch of examples, measuring how wrong its outputs are, and nudging the weights slightly in the direction that would have made them less wrong. Repeated over a huge number of examples, that process lets the network capture patterns no one wrote down.

The name comes from a loose analogy to neurons in the brain, but modern networks are better understood as very large mathematical functions. The weights are the model’s parameters. The transformer is the neural network design behind today’s large language models. How large language models actually work shows where the network fits in.