A gentle walkthrough of how difference penalties automate knot selection in deep neural networks, with intuition-first explanations.

From theory to implementation — understanding how optimal transport reshapes probability distributions for machine learning applications.

An intuitive guide to Positive-Unlabeled learning — what it is, when to use it, and how it connects to classical binary classification.