Cross-Entropy Loss
Domain: AI Fundamentals
🎯 Motivation
A loss function for classification that rewards confident correct predictions and punishes wrong confident ones.
📋 Concrete Example
If the model says 0.9 probability for the correct class, loss is small. If it says 0.1, loss is large.
🔗 Analogy
Like an exam that gives full marks only if you are both correct and confident - guessing right by luck gives little credit.