L1 L2 Regularization
L1 and L2 regularization are techniques in machine learning used to prevent overfitting by adding penalty terms to the loss function. L1 regularization (Lasso) adds the absolute value of coefficients, promoting sparsity and feature selection, while L2 regularization (Ridge) adds the squared value of coefficients, encouraging smaller weights without eliminating features. These methods help improve model generalization by controlling model complexity.
Developers should learn L1 and L2 regularization when building machine learning models, especially in regression and neural networks, to mitigate overfitting on noisy or high-dimensional datasets. L1 is useful for feature selection in scenarios with many irrelevant features, while L2 is preferred when all features are potentially relevant but need weight shrinkage. They are essential in deep learning frameworks like TensorFlow and PyTorch for training robust models.