Loss Functions: Measuring Prediction Error

Loss functions are the neural network's compass during training, quantifying the difference between predictions and truth into a single number that guides learning. They transform complex errors across many examples into a clear signal that the network works to minimize.

Real-world analogy: Think of a basketball coach providing feedback on free throws – the further the shot misses, the more correction needed. Similarly, larger prediction errors result in higher loss values and more significant weight adjustments.

The choice of loss function profoundly impacts which types of errors the model prioritizes fixing. In medical diagnostics, for instance, missing a disease (false negative) might be penalized more heavily than a false alarm (false positive). Common loss functions include Mean Squared Error (MSE) for regression tasks, Cross-Entropy Loss for classification problems, and Huber Loss for handling outliers.