Translations:Overfitting and Regularization/36/en

1. Start with a model large enough to overfit the training data — this confirms the model has sufficient capacity.
2. Add regularization incrementally (dropout, weight decay, augmentation) and monitor validation performance.
3. Use early stopping as a safety net.
4. Prefer more training data over stronger regularization whenever possible — regularization is a substitute for data, not a replacement.
5. Tune the regularization strength ($ \lambda $, dropout rate) using a validation set, never the test set.