Question: What is cross-entropy loss, and how is it used in classification models?

Answer: Cross-entropy loss measures the difference between the predicted probabilities and the actual class labels. It is commonly used as a loss function in classification models, encouraging the model to assign higher probabilities to the correct classes.

Example:

In a neural network for image classification, cross-entropy loss penalizes incorrect predictions with low probabilities.