research

Open reading notes on machine learning, reinforcement learning, neuroscience and the methods used to check a claim.

A paper says what it found. A note here says what it showed, which is a smaller thing, and marks the distance between them. That distance is the whole point of the collection.

Everything is open and reusable under CC BY 4.0: take a note, quote it, build a lecture on it, argue with it — attribution is the only condition.

Where to start

• How to read this

  What the fields at the top of a note mean, and what draft is admitting.

• Notes

  One paper, read closely.

• Essays

  One question across several papers.

• Method

  How claims get checked: baselines, effect sizes, seeds, replication.

What this is not

It is not a paper list and not a survey. A link with a one-line summary is a bookmark, and bookmarks are already free. Each note is written to be readable without the original open, and to be wrong in a findable way if it is wrong.

Nothing here is peer-reviewed. The status field says which of two things a note is: draft means read once, reviewed means read again after a gap with the sources checked line by line.

Corrections

A correction is worth more than an addition. If a note misreads a paper, open an issue with the passage that contradicts it.