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Describe regularization techniques and why they're used.
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Answer / Ravi Gupta
Regularization techniques are methods used to prevent overfitting by adding a penalty term to the loss function during training. Common regularization techniques include L1 (Lasso) and L2 (Ridge) regularization, which encourage sparse or less extreme solutions, respectively. Regularization helps improve the generalizability of models by reducing their complexity.
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