regularization techniques in gradient descent
Regularization techniques in gradient descent are methods used to prevent overfitting in machine learning models. These techniques involve adding a penalty term to the loss function being optimized, which helps to control the complexity of the model and ensure better generalization to unseen data.
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Related Concepts (11)
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