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Learning Math for Machine Learning

blog.ycombinator.com

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Re: Learning Math for Machine Learning

#111

Post by imh »

tMcGrath wrote: post #89
imh wrote: post #74

Earlier quoted context omitted.

Can you give any recommendations for a little bit of differential geometry?

I think the standard reference is probably Spivak's 'Calculus on Manifolds' but this never really did it for me. If you have a background in physics then some combination of Nakahara's 'Geometry, Topology and Physics' and Baez and Muniain's 'Gauge Fields, Knots and Gravity' might be good (I haven't included relativity textbooks as I assume it you have a background in GR then you have enough differential geometry). An…

Chirikjian's book looks really cool! Its website says that in volume 1 "The author reviews stochastic processes and basic differential geometry in an accessible way for applied mathematicians, scientists, and engineers." And I can't tell if that means 'brief review because this is a prereq to the book' or if this is a good first take on it. Do you know which it is?

Re: Learning Math for Machine Learning

#113

Post by _spoonman »

tenaciousDaniel wrote: post #98
_spoonman wrote: post #62

Earlier quoted context omitted.

We should find or start a Slack / Discord where we go through a math textbook and conquer our fear of mathematics together.

holy crap that's an amazing idea

Let’s see where it goes

https://discord.gg/ugV6fht

Re: Learning Math for Machine Learning

#115

Post by pvg »

throwawaymath wrote: post #102
pvg wrote: post #90

Earlier quoted context omitted.

to achieve that "comfortability" "comfort" is a perfectly cromulent word for this.

I was quoting the article; but thank you, I didn't know that. Good to know.

Ah, my mistake. I must have edited it right out when I read the thing and took the quotes for 'I know I'm making up a word but can't think of anything better right this second'.

Re: Learning Math for Machine Learning

#116

Post by kharak »

throwawaymath wrote: post #73
JesseAldridge wrote: post #44

I think a lot of people need to start from the basics because they don't have a good foundation in math. The core problem is schools will push you along if you can somehow produce the correct answer for 70% of the problems on a test. Combine this with intense pressure not to fail and you will very likely end up in higher level math courses with many gaping holes in your foundational knowledge. You thus end up relying…

> Like, I didn't understand how division works -- if someone were to ask me what (3/4) / (5/6) even means conceptually I would not have been able to provide a coherent, accurate explanation. "Uh... it's like taking 5/6 of 3/4... wait no that's multiplication... you need to flip the second fraction over... for some reason..." In case you (or others reading this) still struggle to formalize division, a very nice way to…

Thank you very much for this. I never realized that a) I had no idea how this most fundamental level of math works and b) that it all fits so neatly together.

Re: Learning Math for Machine Learning

#117

Post by mkl »

Tenoke wrote: post #50

>A student’s mindset, as opposed to innate ability, is the primary predictor of one’s ability to learn math (as shown by recent studies). The article seems good overall, but I only skimmed the rest after seeing a citation of a 5-year-old Atlantic article describing disputed and at minimum highly exaggerated findings presented as 'shown in recent studies'.

That may be a bad reference, but there are lots of studies about this. See the book Mathematical Mindsets by Jo Boaler for many references.

Re: Learning Math for Machine Learning

#118

Post by durpleDrank »

ivansavz wrote: post #15

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…

"Check this shit out:" hahaha, I will buy this :)

Re: Learning Math for Machine Learning

#119

Post by js2 »

asafira wrote: post #75
throwawaymath wrote: post #71

Earlier quoted context omitted.

You don't need to do a second bachelors - you really need four or so courses. If you have the patience and dedication you can sit down with the textbooks and work through them on your own.

This. There's always more you might want to learn, but when people talk about these basics, it's really just being super focused in 4 or so classes, not a whole ivy league undergrad curriculum in math. probability & stats, multivariable calculus, and linear algebra will take you a long way.

> They haven't needed it, so they haven't retained it even if they learned it in college.

True for me. I knew all of these from my course work when I graduated with my CS degree in 1996. I haven't used them at all in my career, and so I'd be starting basically from scratch re-learning them.

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