Regularization techniques add a _______ to the loss function to constrain the magnitude of the model parameters.
- Weight penalty
- Bias term
- Learning rate
- Activation function
Regularization techniques add a "Weight penalty" term to the loss function to constrain the magnitude of the model parameters, preventing them from becoming excessively large. This helps prevent overfitting and improves the model's generalization capabilities. Regularization is a crucial concept in machine learning and deep learning.
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