Deep Learning · TEE · section

Choosing a remedy

Regularization and generalization

For overfitting: try L1/L2, dropout, suitable data augmentation, a smaller model, more representative training data and early stopping. For underfitting: improve features or architecture, train adequately, check optimization, or reduce excessive regularization. Evaluate changes on validation data.

Diagram: Read the training and validation pattern before selecting a remedy.. Labels: Match the remedy to the observed problem | Match the remedy to the observed problem | Compare training and validation behavior before changing the model. | Both losses high: underfitting | Model may be too simple. | Training may be insufficient. | Check optimization and features. | Train low, validation high | Possible overfitting. | Use regularization or more data. | Consider early stopping. | A remedy is a choice based on the pattern; no single setting fixes every problem.

Data augmentation makes label-preserving variations of training examples, such as suitable crops or brightness changes in images. It must respect the task: flipping a digit or a medical image may change meaning. Augmentation happens within the training split; do not leak augmented copies into validation.

These are classical tendencies for exam discussion, not exact laws for every deep-network training run. Distinguish model capacity from optimization failure.