Häviömoduulia

Häviömoduulia, also known as the "loss module" or "loss function," is a fundamental concept in machine learning and artificial intelligence. It is a mathematical function that measures the difference between the predicted output of a model and the actual target value. The primary purpose of the loss module is to quantify the error of a model's predictions, which is essential for training and optimizing the model.

The choice of loss module depends on the specific problem being addressed. For regression tasks, where the goal is to predict a continuous value, common loss modules include Mean Squared Error (MSE) and Mean Absolute Error (MAE). In classification tasks, where the goal is to assign a label to an input, popular loss modules are Cross-Entropy Loss and Hinge Loss.

During the training process, the loss module is used to compute the gradient of the loss with respect to the model's parameters. This gradient is then used to update the parameters through optimization algorithms such as Gradient Descent or its variants. The goal is to minimize the loss, thereby improving the model's accuracy and performance.

In summary, the loss module plays a crucial role in the training and evaluation of machine learning models. It provides a quantitative measure of a model's performance and guides the optimization process to improve the model's predictions.