A loss function reflects the error between a target output and an actual output in a neural network. Which of the following is a common loss function in deep learning?

The mean squared loss function is commonly used in deep learning, especially in regression problems. This function calculates the average of the squares of the errors—that is, the difference between the predicted values produced by the model and the actual target values. The squaring of errors ensures that larger errors are penalized more heavily, which is beneficial for model training as it encourages the network to make more accurate predictions.

The mean squared loss function is particularly favored when the relationship between the input and output can be modeled using continuous values, making it suitable for tasks such as predicting numerical values. This function provides a smooth gradient, which is vital for optimization algorithms like gradient descent, allowing the neural network to converge more effectively during training.

While other loss functions like the logarithmic loss, exponential loss, and hinge loss each serve specific purposes in different contexts (such as classification tasks or handling specific types of errors), the mean squared loss function stands out as a foundational approach in deep learning for continuous output predictions.