Training lowers one number, the loss, so how you score mistakes decides what the model learns. Squared error hates big misses, absolute error treats them evenly, Huber does both, and cross-entropy punishes a confident wrong chance without limit. Then add one typo to the data and watch squared error chase it. This lesson needs JavaScript.