ML

Over the next 3-4 months, I’m going to try and self-study ML from the ground up: inside-and-out, under-the-rug, forwards-and-backwards, et cetera. Because I know my crash-and-burn habits when it comes to self-sustained projects all too well, I’m going to be keeping a record here to hopefully motivate myself two-fold. First, who doesn’t like seeing progress/time-lapse/growth content of themselves? I love record-keeping, especially in the form of writing, so hopefully in 5-10 years I can look back on this and see how it played out. Second, I’ll be able to keep myself accountable. Ideally, I wouldn’t need this but alas, I do.

Each entry (hopefully daily?) will represent a day of learning, starting from a rehash of linear algebra and multivariable calculus, all the way up to projects, state-of-the-art models, and hopefully some practical applications of my own! I’ll also be taking notes in Obsidian along the way, and will maybe upload those as well.

Day 2 - Spaces, bases, coding

Alright, today was unfortunately not quite productive like I’d hoped. I kind of abandoned the textbooks because I’ve been itching to get into the stuff that’s actually relevant to ML, like SVD and optimization and stuff, so I went back to this guide I was originally following, and which is...
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Day 1 - Math, math, math

Today was a whole lot of math! Specifically, linear algebra. Actually, not that much, since I took an introduction to linear algebra last year, but as we all know, math instruction rarely sticks. At least for me it didn’t. A complaint I had a lot last year, and which I...
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