Neural networks generalize to unseen data by learning patterns from the training dataset rather than memorizing individual examples. They use features present in the training data to make predictions on similar patterns in new data. This process relies on a well-designed model architecture and sufficient training examples that represent the problem space.
Techniques like regularization help prevent overfitting, which occurs when a model becomes too tailored to the training data and fails on new inputs. Dropout, weight decay, and early stopping are common strategies to enhance generalization by limiting the model's complexity and ensuring it focuses on general patterns rather than noise.
Additionally, a diverse and representative dataset is critical for generalization. If the training data covers a wide range of scenarios, the model is more likely to perform well on new data. Techniques like data augmentation, which artificially increases dataset diversity, can further improve a model's ability to generalize effectively.