Generalization Beats Training Accuracy
Use training and validation behavior to distinguish useful learning from overfitting.
A deep model that fits the training set perfectly may be less useful than a smaller model that generalizes. Generalization is the ability to perform on examples the model did not train on. Deep networks need this discipline because their capacity can be large enough to memorize noise. Watch the gap If training loss falls while validation loss rises, the model is learning details that do not transfer. The train-validation gap is a practical overfitting detector. Regularization adds friction L2 regularization penalizes large weights, dropout makes units less co-dependent, augmentation expands the effective data variety, and early stopping keeps the…
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