Deep Learning - Loss Functions
Mean Squared Error, Mean Absolute Error, Hinge Loss, Huber Loss, L1 Loss, Cross Entropy Loss, NLL Loss, Sparse Entropy, IoU Loss, Dice Loss, Focal Loss, Cauchy Robust Kernel
Regression Losses
Mean Squared Error (MSE)
\[\text{MSE} = \frac{1}{n}\sum_i (y_i - \hat{y}_i)^2\]
Disadvantages:
Sensitive to outliers because errors are squared.
Assumes Gau...
Posted by Rico's Nerd Cluster on January 11, 2022