awesome-deep-learning-papers

Why we included this project

Deep learning moves fast enough that keeping up with every new paper is a losing game, and this list was built for people who want the opposite: a short, deliberate reading path through the field's most-cited work. It holds roughly a hundred papers published between 2012 and 2016, the years that produced many of the field's foundational ideas, grouped by research domain so you can work through them in a sensible order. The maintainer capped the list at a hundred and applied citation thresholds, so each entry had to earn its spot rather than ride on popularity. Anyone getting started in deep learning, or brushing up on the fundamentals, will find it a solid place to begin before tackling newer, more specialized literature. One honest caveat: the list stopped being updated around 2017, so treat it as a historical foundation, not a source of cutting-edge work.