machine-learning-articles

Why we included this project

Keeping up with machine learning writing means wading through a flood of tutorials, and this repository exists to thin that flood down to something manageable. Each month the maintainers screen roughly a thousand to fifteen hundred articles across the machine learning field, rank them by reader engagement and their own scoring algorithm, and publish only the top ten. The result is a compact archive that shows what practitioners actually found worth reading at the time, from February 2018 through mid-2019. It works best as a structured reading list for hunting down foundational explainers and real-world case studies, and as a historical snapshot of the field's hot topics. Since it's a curated set of links rather than runnable code, treat it as a guide to build your own learning path, not as software to deploy.