this post was submitted on 27 Nov 2023
1 points (100.0% liked)
Machine Learning
1 readers
1 users here now
Community Rules:
- Be nice. No offensive behavior, insults or attacks: we encourage a diverse community in which members feel safe and have a voice.
- Make your post clear and comprehensive: posts that lack insight or effort will be removed. (ex: questions which are easily googled)
- Beginner or career related questions go elsewhere. This community is focused in discussion of research and new projects that advance the state-of-the-art.
- Limit self-promotion. Comments and posts should be first and foremost about topics of interest to ML observers and practitioners. Limited self-promotion is tolerated, but the sub is not here as merely a source for free advertisement. Such posts will be removed at the discretion of the mods.
founded 2 years ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
Just me, but I think of busy coworkers with great background in math/stats and 'classic' ML who would ramp up quickly from a list like this. When I onboarded chemists (PhDs) to my ML team at a drug startup, I would send them a similarly dense reading list. With their strong background in physics, it would take them two weeks flat to understand the necessary theory and jargon to be productive (in our niche field).
Didn't mean to say those papers are completely useless, but even for those with a strong Math/ML background I would advise starting with recent survey papers. Reading "Attention is All You Need" is kind of like reading the General Relativity papers of Einstein - cool as a historical curiosity, but not ideal for optimizing expertise acquisition.