understanding loss

Understanding loss is a way for a computer model to measure how well it is performing on a task, like predicting outcomes. It quantifies the difference between the model's predictions and the actual results. A lower loss indicates the model is making more accurate predictions, while a higher loss shows it’s making larger errors. During training, the model adjusts itself to minimize this loss, improving its accuracy over time. Think of it as a guide that helps the model learn by penalizing mistakes and rewarding better predictions, leading to a more reliable and effective system.