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 large weights and reducing overfitting.
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Glossary Terms(5)
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…
Adam Optimizer
Adam (Adaptive Moment Estimation) is a gradient-based optimization algorithm that maintains per-parameter adaptive learning rates using running estimates of th…
Mean Squared Error
Mean squared error (MSE) is a loss function that measures the average of the squared differences between predicted and actual values, commonly used to train an…