Epoch

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.

Gradients shrink through layers, slowing learning in early layers; mitigated by ReLU, residuals, normalization.

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.

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

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