Cross-Entropy Loss
The training loss that measures how surprised the model was by the correct answer.
Cross-entropy is the negative log-probability the model assigned to the true class, −log(p[target]). It’s near 0 when the model is confidently right and explodes when it’s confidently wrong — the signal that trains almost every classifier and language model.