Overfitting and Regularization: Dropout, Weight Decay and Early Stopping
~13 min read
A model that memorizes its training data instead of learning general patterns is overfit — great on training loss, bad on new data. Dropout, weight decay and early stopping are three standard ways to prevent it.
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Key points
- •Overfitting is when training loss keeps improving but validation/test performance stalls or worsens — the model memorized training quirks instead of learning generalizable patterns
- •Dropout randomly zeros out a fraction of neurons every training step, forcing the network to learn redundant, more robust representations
- •Weight decay (L2 regularization) adds a penalty for large weights to the loss, encouraging smoother functions that generalize better
- •Early stopping tracks validation loss and halts training once it stops improving, even if training loss is still falling — directly targeting the overfitting signature
- •Real training runs typically combine all three: dropout and weight decay slow overfitting during training, early stopping is the final safety net