My collection of machine learning paper notes

I think these notes are great, and Vitaly certainly seems like a great person from Twitter (been following for a while now). I just want to spell out the obvious - the biggest (and probably the only) beneficiary of such structured notes is the note-maker.<p>The beginners who come in feeling excited that this will be a great learning resource are probably missing the point. Learning happens when you force yourself to create notes by finding structure in the raw text. Notes are extremely personal, and reading someone else&#x27;s does not have the same emotional connect.<p>Am I suggesting you stop reading notes made by others? Absolutely not! I am suggesting you rather double down on that, except _always_ make your own notes if the objective is learning. Use the excellent public notes to build your own mental models of what makes for good notes.

Very nice. I especially like the structure (What?, Why?, How?, And?) which is shared for every note. Though it has fallen out of popularity, RSS will be useful. Overall the concept kinda reminds me [the morning paper](<a href="https:&#x2F;&#x2F;blog.acolyer.org" rel="nofollow">https:&#x2F;&#x2F;blog.acolyer.org</a>). I wonder if there&#x27;re similar attempts for other fields (math, physics, ...).

I thought they meant a pile of paper notes you could use for machine learning OCR or something.

Hi Vitaly! Nice to see you here on HN. :)<p>I wonder how long you can keep up doing this. I once was motivated to also read a lot (although not strictly one paper a day) but once you get to have more and more deadlines (paper submissions etc) and then approach the end of your PhD, I gave up. Now that this is (mostly) over, I want to read more again.<p>Also, I can recommend to keep a balance of papers close to your own research area (these are anyway a must, if you are serious about it) and also from further away. If you can manage to adopt techniques from other areas&#x2F;fields, this usually results in great things.

I really like this. I recently started a similar project called the &quot;arXiv wiki&quot;. Could you link future paper notes here?<p>For example: <a href="https:&#x2F;&#x2F;arxiv.wiki&#x2F;abs&#x2F;2101.06861" rel="nofollow">https:&#x2F;&#x2F;arxiv.wiki&#x2F;abs&#x2F;2101.06861</a>

This is cool stuff. I am planning to do something like this myself.

Thank you!

notes on machine learning papers

A bit of a tangent, but Notion is performing well for a post on the HN front page, even with equations.