What was the difference between l1 & l2 regularization?
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Answer / Pushpendra Raghuvanshi
L1 and L2 regularization are both techniques used to prevent overfitting in machine learning models by adding a penalty term to the loss function. However, they differ in the form of this penalty term: L1 regularization adds a term that encourages sparse solutions (i.e., solutions with many zero coefficients) by using the absolute value of the coefficients, while L2 regularization adds a term proportional to the square of the coefficients, which discourages large coefficients but does not force them to zero.
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