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 over-relying on any single unit and reducing overfitting.
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Glossary Terms(5)
Batch Normalization
Batch normalization is a neural network training technique that normalizes the inputs to each layer using the mean and variance computed over a mini-batch, the…
Layer Normalization
Layer normalization is a neural network normalization technique that normalizes activations across the features of a single training example, rather than acros…
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…