Loss Function
A mathematical formula that calculates the difference between a model's predicted output and the actual ground truth, guiding the optimization process.
Think of It Like This
Like a coach's harsh grading system that tells an athlete exactly how far they missed the target so they can correct their aim.
The primary goal of model training is to minimize this loss value using algorithms like gradient descent. Different tasks require distinct loss functions; for example, Mean Squared Error (MSE) is used for regression, while Cross-Entropy is standard for classification. It serves as the absolute directional compass for backpropagation.