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Read all the leading papers, many times, to get a deep understanding, the writing quality is usually pretty low, but the information density can be very high, y
by stephc_int13 4y ago
Read all the leading papers, many times, to get a deep understanding, the writing quality is usually pretty low, but the information density can be very high, you'll probably miss the important details the first time.
Most medium and low-quality papers are full of errors and noise, but you can still learn from them.
Get your hands dirty with real code.
I would take a look at those:
https://github.com/geohot/tinygrad https://github.com/geohot/tinygrad
https://github.com/ggerganov/whisper.cpp https://github.com/ggerganov/whisper.cpp
- zone411 4y agoDisagree. The information density of 99% of ML research papers is not high. It's usually just one idea that's often just an evolutionary modification of something that already exists, or something that worked in a different ML area applied here, along with an intro, maybe some supporting math, and some experiments. The information density might seem high if you're not familiar with previous papers that are referenced. I also don't agree that medium quality (how to decide what's medium quality anyway? includes main conferences?) papers are full of errors.
- stephc_int13 4y agoThis comes from my experience with Computer Vision papers about ten years ago. I've done some work in the field, and I've seen errors in almost all papers. Sometimes they are not really errors, but they give a solution that only works in the very specific context they tested it and completely falls apart anywhere else. I've lost countless hours and days studying and replicating papers. Of course, ML could be an entirely different experience.
- zone411 4y agoI do expect that it was different back then. The replication situation has improved somewhat lately but it's still not great.