Regularization - Early Stopping, Ridge Regression (L2) and Lasso Regression (L1) [Lecture 1.6]

"How to prevent overfitting by regularization? What is the difference between L1 and L2 regularization?"

___________________________________________

___________________________________________
Part 1: Regression in Machine Learning and the Linear Regression Model

Part 2: The Different Cost Functions for Regression - Understand MAE, MSE, WMAE

Part 3: How Model Training with Gradient Based Minimization works

Part 4: Data Preprocessing - Normalization, Outliers, Missing Data, Variable Transformation

Part 5: Cross Validation - How to Select the Best Machine Learning Model?

Part 6: Regularization - Early Stopping, Ridge Regression (L2) and Lasso Regression (L1)

___________________________________________
- Early stopping: stop gradient based minimization befor overfitting is a problem
- Ridge regression: Tikhonov regression, L2 term
- Lasso regression: L1 term tends to set parameters to zero in comparision to the L2 term.