papers

Why we included this project

This repository is a hand-curated reading list of influential deep learning papers, each paired with a short written review that explains the core idea in plain terms. For researchers, graduate students, or engineers trying to get oriented in the field, it works as a guided tour rather than a raw arXiv dump: you get the paper link, the venue and authors, and a digestible summary that helps you decide whether the full text is worth your time. The selection leans toward foundational work in computer vision, sequence modeling, and reinforcement learning from 2016 to 2018, so it is especially useful for understanding the ideas that later systems build on. Because the reviews are written by a single author with a consistent voice, the collection reads like a coherent set of study notes rather than an anonymous aggregation. It is a learning resource, not deployable software, and that is the point: the value is in the reading, not in running anything.