Regularization in deep learning#overfitting#deeplearning#machinelearning#artificialintelligence#ai
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Regularization is a technique used in machine learning to prevent overfitting by adding a penalty term to the loss function. It discourages complex models by constraining the weights (e.g., L1/L2 regularization) or applying dropout, leading to better generalization on unseen data. Common types include:
1.L1 (Lasso): Encourages sparsity by shrinking some weights to zero.
2.L2 (Ridge): Penalizes large weights, keeping them small but non-zero.
Regularization improves model robustness and performance.
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