Loss Function Design And Implementation

via “loss function design and implementation for different tasks”


Unique: Derives loss functions from probabilistic principles (maximum likelihood for classification, expected squared error for regression), then shows the implementation and how to compute gradients, connecting theory to practice

vs others: More principled than just listing loss functions, more practical than pure probability theory, and includes implementation details that documentation often skips