Overfitting

Symptoms: Large gap between training and test performance.
Mitigation: Regularization, data augmentation, early stopping, more data.
Diagnostics: Learning curves, error analysis, ablation studies.

Fundamentals

Model memorizes noise; performance drops on new data.

Overfitting occurs when a model learns random patterns in the training data instead of general ones.