Posts tagged “probabilistic-ml”

3 posts found

Why Uncertainty Matters: From Confidence to Calibration

Why AI systems that express calibrated uncertainty are safer and more useful — covering overconfident models, epistemic vs aleatoric uncertainty, and calibration metrics including ECE and reliability diagrams.

Jul 13, 2026•10 min read

ai-tutorialstutorialintermediate

Gaussian Processes Explained: From Kernels to Deep GPs

A step-by-step introduction to Gaussian Processes — what they are, how kernel functions define them, how to do GP regression, and how Deep GPs extend them to multi-layer architectures.

Jul 13, 2026•9 min read

ai-tutorialstutorialgaussian-processes

Attention Is All You Need... Is a Kernel

How scaled dot-product attention is secretly Nadaraya–Watson kernel regression — and what that reveals about the GP–Transformer duality, the Deep GP revival, and why uncertainty is the missing ingredient for world models.

Jul 12, 2026•12 min read