yes ML algo are a primary focus because I don't know much about physics (besides being trained as a mechanical engineer so I do know about DE and used multiple FE simulations, including Nastran, Patran, Ansys Fluent for airplane design and many other computational dynamics tool for melted iron and steel but that was only as a user)
one thing I will definitely not implement from scratch is gradient boosted trees given the work that is being done in XGBoost and LightGBM
note that I didn't reimplement from scratch, just reused linear algebra components from Lapack
because I don't know much about physics
What are you thinking of here? I do know a thing or two of course. I can maybe help.
regarding implementing from scratch: for me as a - still sort of - beginner I often struggle to find the proper algorithms that I should implement. For many areas there's so many different ones with pros and cons one the one hand or the "algorithms" are not actually what's usually done implementation wise, because there's always a bunch of things, which for some people are obvious things to do, efficiency wise. And to be honest I'm pretty slow at converting math to code without running into a huge number of numerical problems, at least.
Oh perhaps it could be worthwhile at least trying to combine doing it from scratch and getting "heavy inspiration" from SciKit
With the physics things I should also be able to lend a helping hand 🖐️ And I agree that some things are really hard to understand hoe to implement from theory
Looking at how others have implemented things really is the thing that had taught me the most
Getting a working implementation is the most important first step in my opinion and then we can always switch the algorithm if we find a faster one in the future and hopefully we will be able to maintain the same function calls for the users
Getting a working implementation is the most important first step
And to be honest I'm pretty slow at converting math to code without running into a huge number of numerical problems, at least.
Reproducing papers is almost impossible
I don't try it whether in Machine Learning or Cryptography or blockchain
I look into the implementations
multithreading is even worse
that makes me feel a little better about myself ;)