What Is Regularization? L1, L2, Dropout, and Early Stopping Explained

Regularization modifies training objectives, model architecture, or stopping criteria to reduce overfitting and improve generalization in AI models. It involves identifying overfitting signs, applying constraints like L1, L2, dropout, tuning strength, and validating performance on new data.