Neural Networks Learn Through Backpropagation and Optimization

Neural networks are not explicitly programmed with every possible answer. Instead, they learn from data by training on millions of examples. This process involves forward passes, error calculation, and backpropagation, where the network adjusts its internal parameters through tiny weight updates. Over billions of training steps, the model reduces error and discovers complex patterns. This learning method allows modern language models to generate new outputs by understanding statistical representations of language, images, and more.