[bcheung]

Agreed. There's too much of an academic paper culture around ML. It makes the field very unapproachable. It's as if people don't actually use it for anything practical and they only care about the science and math.

It is getting better though. It's great to see people post articles like this and even if it has been done before seeing different approaches and seeing it written in different ways goes a long ways towards educating people.

I'm hoping the JavaScript ML / DL ecosystem expands more since that would greatly accelerate its adoption and allow more developers access to ML.

[doggo_]

It's definitely getting better. With fast.ai, and Tensorflow coming with examples and docs to get started, I was able to make a proof of concept in a few hours to solve a real problem for real money.

It feels like we are on the cusp of non-experts being able to get real results and them being able to improve on classifiers for their domain. It almost feels like right before Rails took the world by storm.

[riku_iki]

Recent tendency in ML/DL academic papers is applying described approach on some public benchmark, and showing improvement over current State of The Art results.

[bcheung]

ML/DL is certainly doing better than most but it still has the academic culture. It is increasingly more common to release code on github so the result can be reproduced and people can adapt their research.