<ul><li>Mean Squared Error (MSE): Your go-to for standard regression problems. Punishes larger errors more severely.</li><li>Mean Absolute Error (MAE): When outliers exist, MAE remains robust by treating all error magnitudes linearly.</li><li>Huber Loss: The best of both worlds — combines MSE and MAE properties by being quadratic for small errors and linear for large ones.</li><li>Log-Cosh: A smooth approximation of MAE that’s differentiable everywhere while maintaining outlier resistance.</li></ul>

The Art of Loss Functions: Your Guide to Training Better ML Models

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