DNN Foundations: Regularization, Dropout, and Overfitting Prevention
Contents
Overview
Essential concepts for building effective deep neural networks, with focus on regularization techniques and preventing overfitting.
Preventing Overfitting in DNNs
Regularization
Adding penalty terms to discourage large weights during training.
L1 Regularization
- Formula: λ|w|
- Encourages exact zeros in weights
- Useful for automatic feature selection
L2 Regularization
- Formula: λ·(1/2)w²
- Continuous weight decay toward zero
- More stable than L1 without collapsing to exact zeros
Max Norm Constraints
Restricting weight magnitudes to prevent them from growing too large.
Dropout
- Randomly deactivating neurons during training (with probability p)
- Prevents co-adaptation of neurons
- Inverted dropout scales outputs by 1/p during training for consistent expected values
Based on “Fundamentals of Deep Learning” and refined through practical experience.