Regularization

➺ Core Techniques:

  • Weight decay (L1/L2)
  • Dropout variants
  • Data augmentation
  • Label smoothing 

    • ➺ Implementation Details:

    Dropout Strategies
  • Standard dropout (0.1-0.5 rate)
  • Spatial dropout
  • Stochastic depth
  • DropBlock for CNNs
    • Weight Regularization 
  • L1 (Lasso): Sparse features
  • L2 (Ridge): Weight magnitude control
  • Elastic Net: Combined L1+L2
  • Custom decay schedules 
    • Advanced Methods
  • Mixup augmentation
  • CutMix/CutOut
  • Random erasing
  • Feature-space augmentation

    • ➺ Monitoring & Tuning:

  • Overfitting detection
  • Validation curves
  • Regularization strength
  • Cross-validation strategies