Loss functions are essential components in machine learning and deep learning, guiding the training process of models by quantifying the difference between predicted and actual outcomes. They serve as objective functions that algorithms aim to minimize or maximize, thereby improving model performance. Various types of loss functions exist, each suited for different tasks, such as regression or classification. Common examples include Mean Squared Error (MSE) for regression tasks and Cross-Entropy Loss for classification tasks. Understanding and selecting the appropriate loss function is crucial for developing effective machine learning models.