Loss Function

The loss function tells the model how wrong its prediction was. After each guess during training, the model compares its output to the correct answer and gets a score for how far off it was. This error signal is used to adjust parameters — nudging the model slightly toward better predictions. Training is essentially the process of minimizing this score over billions of examples.