A loss function is a mathematical function that measures the difference between the predicted output and the true value (ground truth). It quantifies how well or poorly the neural network performs on a given task, and the goal of training is to minimize this loss.
Common loss functions include Mean Squared Error (MSE) for regression tasks and Cross-Entropy Loss for classification tasks. For example, in binary classification, Cross-Entropy measures the difference between predicted class probabilities and true class labels.
Loss functions are essential for backpropagation, the process where the network adjusts its weights based on the gradient of the loss. Choosing the right loss function is crucial for the model to learn effectively and converge towards optimal solutions.