A neural network composes parameterized transformations and nonlinear activations. Training uses gradients to adjust parameters. More capacity can represent more complex relationships, but it can also fit noise or memorize examples.
Generalization means performing well on relevant unseen inputs. Split data before fitting learned preprocessing, and keep related records together when they could leak information across splits. A validation set supports development decisions; a protected test set supports a later assessment.
Overfitting appears when training performance improves while held-out behavior worsens. Address it through data quality, appropriate model capacity, regularization, and evaluation design. A larger network does not repair a mislabeled target or an unrealistic test set.
Exercise: design train, validation, and test splits for customer tickets containing duplicates and time-dependent policies. Explain why a random row split may be misleading.
Check: no near-duplicate group crosses the boundary, and the evaluation reflects the intended deployment setting.