An Introduction to Cross-Entropy Loss Functions

Cross-entropy loss is a significant loss function, particularly in classification tasks, as it measures the difference between two probability distributions, reflecting how well the model predicts actual outcomes. It can be considered a surrogate for other more complex loss functions and provides non-asymptotic guarantees and an upper boundary on the estimation error of the actual loss based on the error values derived from the surrogate loss. Cross-entropy is widely used in deep learning models, especially when interpreting outputs of neural networks that utilize the softmax function. It is also integral to understanding the nuances of different loss functions and their impact on model optimization.