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When I was starting out in machine learning, as a programmer with the most rudimentary calculus background, it was easy to derive algorithms that had terms like
by jey 9y ago
When I was starting out in machine learning, as a programmer with the most rudimentary calculus background, it was easy to derive algorithms that had terms like "gradient w.r.t X of log(det(inv(λI + A X A')))" which absolutely stumped me when trying to derive the gradient by hand by elementwise partials.
However, thanks to Minka's notes and the Matrix Cookbook, I was able to eventually get a handle on easy techniques for these derivations! It's certainly no substitute for getting a handle on the theory first by studying a textbook, but these pattern-matching shorthands are important practical techniques.
- platz 9y agoHow did you start in machine learning. Did you come from a top 10 school?
- jey 9y agoNo, I failed out of university and am self-taught. I spent many years deliberately underemployed working on self-study and coding up projects at the limit of my abilities. I had decided to bet on investing in myself instead of just carrying out some boss’s wishes, which would be more optimized to benefit the company than my own growth and development. Here’s a great resource if you’re starting out today: http://datasciencemasters.org http://datasciencemasters.org
- asddddd 9y agoThanks for the link! That's more or less the road I'm starting down right now, albeit with likely a bumpier past (dropped out of HS in addition to college and spent a few years doing almost nothing productive.)
- digitalshankar 9y agoInspiring story!!