Techniques that prevent a model from overfitting by adding constraints during training.
Techniques that prevent a model from overfitting by adding constraints during training. L1 and L2 regularization add penalty terms to the loss function. Dropout randomly disables neurons. Weight decay gradually shrinks weights. All help the model generalize better to unseen data.
When a model memorizes the training data so well that it performs poorly on new, unseen data.
A regularization technique that randomly deactivates a percentage of neurons during training.
A mathematical function that measures how far the model's predictions are from the correct answers.
A mathematical function applied to a neuron's output that introduces non-linearity into the network.
An optimization algorithm that combines the best parts of two other methods — AdaGrad and RMSProp.
Artificial General Intelligence.
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