Learning Rate

Iterative method that updates parameters in the direction of negative gradient to minimize loss.

Number of samples per gradient update; impacts compute efficiency, generalization, and stability.

One complete traversal of the training dataset during training.

Halting training when validation performance stops improving to reduce overfitting.

Nonlinear functions enabling networks to approximate complex mappings; ReLU variants dominate modern DL.

Plots true positive rate vs false positive rate across thresholds; summarizes separability.

Scalar summary of ROC; measures ranking ability, not calibration.

The degree to which predicted probabilities match true frequencies (e.g., 0.8 means ~80% correct).

A gradient method using random minibatches for efficient training on large datasets.