Neural Network Lexicon

Regularization

Short Definition

Regularization reduces overfitting by constraining model complexity.

Definition

Regularization refers to techniques that limit how complex a neural network can become during training. Instead of allowing weights to grow arbitrarily large or models to perfectly fit training data, regularization introduces constraints or penalties that encourage simpler solutions.

The goal of regularization is not to reduce training performance, but to improve generalization by discouraging the model from learning noise instead of meaningful patterns.

Why It Matters

Without regularization, neural networks often memorize training data and fail on new inputs.

How It Works (Conceptually)

  • Penalize large weights
  • Reduce effective model capacity
  • Encourage smoother solutions

Minimal Python Example


Python
loss = data_loss + lambda_reg * (weight ** 2)


Common Pitfalls

  • Applying excessive regularization
  • Using regularization without validation
  • Ignoring its interaction with model size

Related Concepts

  • Overfitting
  • Bias–Variance Tradeoff
  • Dropout