11–20 of 119 posts
Thanks for posting this!! Was actually searching for this the other day here on HN and found a link to the https://github.com/mml-book/mml-book.github.io . haven't checked it out yet but the links in the OP look solid.
For those who don't know, please check out 3blue1brown videos on youtube for a better understanding of concepts like Linear algebra and other things required for machine learning. Thank me later.
Thanks for posting this!! Was actually searching for this the other day here on HN and found a link to the https://github.com/mml-book/mml-book.github.io . haven't checked it out yet but the links in the OP look solid.
Looks interesting! Have you gone through it yourself? And how does it compare to other resources?
> As soft prerequisites, we assume basic comfortability with linear algebra/matrix calc [...] >
That's a bit of an understatement. I think anyone interested in learning ML should invest the time needed to deeply understand Linear Algebra: vectors, linear transformations, representations, vector spaces, matrix methods, etc. Linear algebra knowledge and intuition is key to all things ML, probably even more important than calculus.
Book plug: I wrote the "No Bullshit Guide to Linear Algebra" which is a compact little brick that reviews high school math (for anyone who is "rusty" on the basics), covers all the standard LA topics, and also introduces dozens of applications. Check the extended preview here https://minireference.com/static/excerpts/noBSguide2LA_previ... and the amazon reviews https://www.amazon.com/dp/0992001021/noBSLA#customerReviews
This is excellent. Thank you for taking the time to write it. I don't know what is it about math -- especially when it involves manipulation of symbols as opposed to pictures or lay language -- that turns off so many people. The fact that so many software developers "don't like math" is ironic, because they're perfectly happy to manipulate symbols such as "x", "file", or "user_id" that stand in for other things every…
I can tell you at least part of it, from my subjective perspective. I tend to "think" in a very verbal fashion and I instinctively try to sub-vocalize everything I read. So when I see math, as soon as I see a symbol that I can't "say" to myself (eg, a greek letter that I don't recognize, or any other unfamiliar notation) my brain just tries to short-circuit whatever is going on, and my eyes want to glaze over and jump to the stuff that is familiar.
OTOH, with written prose, I might see a word I don't recognize, but I can usually work out how to pronounce it (at least approximately) and I can often infer the meaning (at least approximately) from context. So I can read prose even when bits of it are unfamiliar.
There's also the issue that math is so linear in terms of dependencies, and it's - in my experience - very "use it or lose it" in terms of how quickly you forget bits of it if you aren't using it on day-in / day-out basis.
Here is a nice "cheat sheet" that introduces many math concepts needed for ML: https://ml-cheatsheet.readthedocs.io/en/latest/ > As soft prerequisites, we assume basic comfortability with linear algebra/matrix calc [...] > That's a bit of an understatement. I think anyone interested in learning ML should invest the time needed to deeply understand Linear Algebra: vectors, linear transformations, representations, vect…
I recommend the No Bullshit books for anyone with no real math background past trig to get their feet wet, and/or anyone who hasn't done any serious math study for years. https://minireference.com/
MATH & PHYS book: https://minireference.com/static/excerpts/noBSguide_v5_previ...
LA book: https://minireference.com/static/excerpts/noBSguide2LA_previ... + free tutorial: https://minireference.com/static/tutorials/linear_algebra_in...
This is excellent. Thank you for taking the time to write it. I don't know what is it about math -- especially when it involves manipulation of symbols as opposed to pictures or lay language -- that turns off so many people. The fact that so many software developers "don't like math" is ironic, because they're perfectly happy to manipulate symbols such as "x", "file", or "user_id" that stand in for other things every…
Also, for me personally, it's just such a drag to learn all the notation. After the fact, I've always thought, "Wow, that's all this means?" but while I'm learning, I feel helpless. It doesn't feel like I have any way to google it. My professors never actually want to sit down and explain it to me. All the pages of math equations always look so intimidating. It's just such a drag.