What Is a Loss Function?

What Is a Loss Function?

A loss function measures how far the model’s predictions are from the correct answers. It gives the model feedback so it knows what to improve.

Why Loss Functions Matter

• Guide training: Lower loss means better learning.
• Enable optimization: Used to adjust model weights.
• Measure accuracy: Tracks training progress.

Common Loss Functions

• Cross-entropy loss
• Mean squared error (MSE)
• Binary classification loss
• Adversarial loss (GAN-specific)

Loss Function FAQ

Does low loss always mean a good model?

Not always—overfitting can trick loss values.

Is loss used after training?

It’s mainly used during training, not after.