Neural Networks: Core Concepts

Artificial neural networks are parameterized functions built from layers that transform numeric representations.

Forward propagation computes predictions.

Training/validation loss curves, held-out metrics, gradient inspection, ablation tests, calibration, and comparison with baselines show whether a neural model is learning useful structure rather than merely fitting noise.