Machine-Learning-Tokyo/papers-with-annotations
Hand-drawn margin notes for deep-learning papers
A community effort to make dense AI literature readable by covering papers in handwritten diagrams, stickers, and plain-English asides.
★826 stars Learning
Not currently ranked — collecting fresh signals.
star history
What it does
Machine Learning Tokyo curates AI and deep-learning papers that have been manually annotated with illustrations, handwritten notes, and short explanations. The goal is to lower the barrier to understanding dense technical writing by adding visual context directly onto the PDFs. Topics span object detection, CNNs, unsupervised learning, and cognitive science.
The interesting bit
The annotations are not digital overlays in the LaTeX sense; they are literal iPad scribbles made in Notability with an Apple Pencil, complete with imported figures and stickers. It treats the research paper like a textbook you are allowed to write in.
Key highlights
- Curated collections for object detection, CNNs, unsupervised learning, and cognitive science.
- Each paper is marked up with handwritten notes, imported figures, and brief explanations of jargon and prior work.
- Accepts community submissions via pull request for AI, ML, neuroscience, and cognitive-science papers.
- Has been highlighted by David Ha and Analytics Vidhya.
Verdict
Best for visual learners who want a head start on seminal papers; less useful if you need pristine source PDFs or full-text search.
Frequently asked
- What is Machine-Learning-Tokyo/papers-with-annotations?
- A community effort to make dense AI literature readable by covering papers in handwritten diagrams, stickers, and plain-English asides.
- Is papers-with-annotations open source?
- Yes — Machine-Learning-Tokyo/papers-with-annotations is open source, released under the MIT license.
- How popular is papers-with-annotations?
- Machine-Learning-Tokyo/papers-with-annotations has 826 stars on GitHub.
- Where can I find papers-with-annotations?
- Machine-Learning-Tokyo/papers-with-annotations is on GitHub at https://github.com/Machine-Learning-Tokyo/papers-with-annotations.