L1 Regularization
L1 regularization is a technique that adds a penalty proportional to the sum of the absolute values of a model's weights to its loss function, encouraging sparse solutions where many weights become exactly zero.
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Glossary Terms(3)
Dropout Regularization
Dropout is a regularization technique that randomly deactivates a fraction of a neural network's neurons during each training step, preventing the network from…
L1 Regularization
L1 regularization is a technique that adds a penalty proportional to the sum of the absolute values of a model's weights to its loss function, encouraging spar…
L2 Regularization
L2 regularization is a technique that adds a penalty proportional to the sum of the squared values of a model's weights to its loss function, discouraging larg…