Deep Learning, Machine Learning, Artificial Intelligence, Loss Function, Neural Networks.
errors. This makes Huber loss more robust to outliers than MSE loss. The Huber loss function is defined as follows: L(y_pred, y_true) = 1/n * sum(0.5 * (y_pred - y_true)^2) if |y_pred - y_true| <= delta 1/n * sum(delta * |y_pred - y_true| - 0.5 * delta^2) otherwise where n is the number of samples,