Overfitting and regularization in Machine Learning #ai #machinelearning #overfitting #regularization

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Overfitting is a well-known problem in machine learning. It occurs when the model learns too many specific details of the data, leading it to lose all sense of generalization on new examples.
To prevent overfitting, we can apply regularization techniques such as Weight decay using the L1 and L2 variants (or Lasso and Ridge), Dropout or Batch Normalization.

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