Neura saysRegularization (L1, L2, dropout) curbs overfitting so models generalize.
Overfit models memorize training noise. L2 (weight decay) penalizes large weights, encouraging smoother functions; L1 pushes weights to zero (feature selection / sparsity); dropout randomly disables neurons during training so the net can't rely on any one path. Each constrains the model to generalize rather than memorize — essential whenever training accuracy outpaces validation.
Power-ups you unlock
L2 penalizes large weights (smoothness)
L1 zeros weights (sparsity/selection)
Dropout disables random neurons in training
Use when train ≫ validation accuracy
The Overfit Ogre attacks — common mistakes
No regularization on a high-capacity model
Dropout left on at inference time
Over-regularizing → underfitting
Boss battleMatch L1, L2, and dropout to what each one does.
Example code
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<body style="background:#06040d;color:#e6e0ff;font-family:monospace;padding:20px"><pre>L2 → smaller weights (smooth)
L1 → zero weights (sparse)
dropout → randomly drop neurons in training</pre></body></html>