ML From Scratch
A self-lead refresher in basic ML algorithms
This project was inspired by a hackathon in the Spring of 2016 when I was working on the engineering team at Magnetic. At Magnetic I had been working on our machine learning systems that used Vowpal Wabbit, and in this hackathon we attempted to implement a similar logistic regression solver in the Go language - which we called Gowpal Wabbit (a large part of projects at Magnetic involved arguing about how to construct a witty/punny name, Gowpal Wabbit was not our best work). My colleague Dan Crosta wrote about what he learned about logistic regression from the process.
While I had learned a lot about the most commonly used algorithms in grad school and at work, writing logistic regression from scratch and teaching a team of software engineers the math and intuition beyond the gradient descent solver made me think much harder about the various choices that go into writing a working implementation. It was a surprisingly educational experience.
Since then I've changed jobs, and after a traveling a lot in the Summer and Fall of 2016 I've found some free time again. I am (intermittently) writing some of the more commonly used ML algorithms from near-scratch and comparing their performance (both in terms of predictive power and computational efficiency) versus scikit-learn. While I feel that I had a pretty good handle on this subject beforehand, I think that forcing myself to reinvent the wheel has been worthwhile.
These algorithms exist for my re-education and very little else. My algorithms will hopefully be just as good at prediction as scikit-learn's options, but theirs are more fully-featured and much faster (since they're written in Cython). There is no reason anybody should be using my algorithms unless they find my code educational.
Note: this project is completely unrelated to (and was started before) the very similarly named repo ML-from-scratch. I guess my name wasn't as original as I had hoped.