papers-I-read

Why we included this project

This is a personal, hand-curated reading list of influential machine learning and deep learning papers, kept by a researcher who splits time between industry labs and academic groups. Each entry comes with the author's own summaries and study notes, so you get a worked example of how an experienced practitioner actually reads a paper and decides what matters in it. The selection is eclectic on purpose: foundational work on hypernetworks and continual learning sits alongside practical systems papers like GPipe, YouTube's recommendation architecture, and ad-click prediction at Facebook. For engineers and ML students who want a guided way into the literature without drowning in arXiv listings, that mix is the point, since the author's taste does the filtering for you. It is a reference and learning resource rather than software you would deploy, best treated as a reading companion or a model for keeping your own study notes.