Intro to Computational Biology
Regularization techniques are methods used in machine learning and statistical modeling to prevent overfitting by adding a penalty term to the loss function. This encourages the model to maintain simplicity by avoiding excessively complex models that fit the training data too closely, leading to poor generalization on unseen data. Regularization is essential for building robust deep learning models that can perform well on new, unseen examples.
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