# Choosing a remedy

Subject: Deep Learning

Exam: MT2

Reader: https://deep-learning-study-room.vercel.app/#mt2/read/6/mt2-c6-s6

Canonical: https://deep-learning-study-room.vercel.app/read/dl/mt2/section/mt2-c6-s6/

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.

![Read the training and validation pattern before selecting a remedy.](https://deep-learning-study-room.vercel.app/assets/figures/v_remedies.svg)

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.

Read the training and validation pattern before selecting a remedy. Tap the diagram to enlarge.

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.

| Change | Usual bias tendency | Usual variance tendency |
| --- | --- | --- |
| Stronger weight regularization | Increase | Decrease |
| More hidden units with other factors fixed | Decrease | Increase |
| Dropout | Increase | Decrease |
| More representative data from same distribution | Roughly unchanged model bias | Decrease |

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