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several regularization techniques that can reduce the risk of overfitting the training set.

From

Cover of Hands-On Machine Learning with Scikit-Learn and TensorFlow

Note

A regression model that uses L1 regularization technique is called Lasso Regression and model which uses L2 is called Ridge Regression.
Ridge regression adds “squared magnitude” of coefficient as penalty term to the loss function.
Lasso Regression (Least Absolute Shrinkage and Selection Operator) adds “absolute value of magnitude” of coefficient as penalty term to the loss function.