calibration
4 free lessons tagged calibration across AI, Robotics. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
Classification: Logistic Regression and Metrics That Do Not Lie
A classifier outputs a score; you choose where to cut it. Learn how logistic regression and log loss work, why 99% accuracy can be worthless on rare events, how precision, recall and PR curves expose it, and how to pick a threshold from the real costs of each mistake.
GPTQ, AWQ, and What Calibration Data Buys You
Round-to-nearest minimises the wrong thing. The algorithms that make 4-bit weights usable minimise output error instead, using a small calibration set. This lesson works through GPTQ's Hessian-guided error compensation and AWQ's activation-aware channel scaling, why AWQ tolerates calibration mismatch better, how much data you need, and where QAT and QLoRA fit.
Abstention: Building a System That Can Say It Does Not Know
The most valuable behaviour an AI system can have is refusing to answer when it should not. This lesson covers calibration and why stated confidence is unreliable, selective prediction and the coverage-accuracy trade, conformal methods that give guarantees, designing abstention users accept, and measuring all of it in production.
Cameras and Visual Perception
From photons to 3D geometry: the pinhole model, intrinsic matrix K, lens distortion, feature matching, stereo depth, and where CNNs help (and fail) in robot perception pipelines.