Batch Size

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

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

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.

Activation max(0, x); improves gradient flow and training speed in deep nets.

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).

Controls the size of parameter updates; too high diverges, too low trains slowly or gets stuck.