A loss function is a crucial component in machine learning and deep learning, serving as a metric to evaluate how well a model’s predictions align with the actual outcomes. It quantifies the difference between the predicted values and the true values, guiding the optimization process during training. By minimizing the loss function, the model adjusts its parameters to improve accuracy. Loss functions can be categorized into two main types: those used for classification tasks, which deal with discrete outcomes, and those for regression tasks, which handle continuous values. Selecting the appropriate loss function is vital for effective model training.