Conclusion

Generalization is the reason validation exists.

Training loss tells whether the model fits examples it trained on. Validation behavior gives evidence about whether the learned pattern transfers.

You have now seen the first regularization tools:

• weight decay discourages unnecessarily large weights
• dropout perturbs training
• early stopping uses validation behavior
• data augmentation adds safe variation
• capacity must be read together with dataset size and task evidence

The next chapter turns these ideas into a debugging checklist. It asks how to trust a small model run using metrics, curves, gradients, activations, and sanity checks.