What it does This repo is a personal reading log: markdown summaries of influential deep-learning papers from 2014–2018, organized by year with links to originals. Think of it as a study buddy who already did the hard work of distilling ResNet, YOLO, Neural Turing Machines, and roughly 30 others into digestible notes.

The interesting bit The value isn’t the code—there isn’t any. It’s the editorial judgment. The author picked papers that actually shaped the field (attention mechanisms, GANs, residual networks) rather than chasing every arXiv preprint. Each summary lives as a separate markdown file, making the repo trivial to browse or grep.

Key highlights

• Covers foundational work: Seq2Seq, BatchNorm, Spatial Transformers, Neural Turing Machines
• Each entry pairs the original paper with a short markdown review
• Chronological organization makes it easy to trace how ideas evolved (2014 CNNs → 2017 GANs → 2018 world models)
• No dependencies, no build step—just text
• 572 stars suggest it resonated with other time-constrained researchers

Caveats

• Stopped updating around 2018; misses the transformer explosion and everything since
• Summaries vary in depth—some are thorough, others look like quick sketches
• A few review links in the README appear to be relative paths that may 404

Verdict Worth bookmarking if you’re a student or researcher trying to catch up on pre-2018 fundamentals. Skip it if you need state-of-the-art coverage or code implementations; this is a reading list, not a framework.