Cross-entropy loss is a widely used loss function in classification tasks that measures the difference between two probability distributions: the predicted probability distribution and the true distribution of labels. It quantifies how well the predicted probabilities align with the actual outcomes, making it essential for optimizing models, especially in scenarios where softmax outputs are used to generate class probabilities.
congrats on reading the definition of cross-entropy loss. now let's actually learn it.