OpenDecode

Introduction

Plain-language explainers for the research papers this course cites most — what problem each one solved, the key idea, and why it mattered.

Every lesson and glossary entry in this course eventually points back to a paper — the actual publication where an idea was first shown to work. This section is short explainers for the ones cited most often: what problem the authors were solving, the key idea in plain language, and why it ended up mattering enough to reshape the field. Not a summary of the abstract — this site already has lessons and a glossary for that; these pages are about the paper itself as a historical and technical event.

Each page links out to the paper on arXiv for anyone who wants to read the real thing. Grouped roughly by topic in the sidebar. This is a seed list, not a complete literature review — more will be added as the course grows.

Perceptrons

Minsky & Papert, 1969 — a rigorous mathematical critique of the single-layer perceptron that stalled neural network research funding for most of a decade, the first AI winter.