Answer / Saumitra Kumar Mishra

L1 and L2 regularization are techniques used to prevent overfitting in machine learning models by adding a penalty term to the loss function. The key difference lies in the type of penalty: L1 regularization uses an absolute value (|w|) of the weights, while L2 regularization employs the square (w^2) of the weights. L1 regularization tends to produce sparse solutions (i.e., zeroing out some coefficients), whereas L2 regularization results in more continuous solutions.

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