It’s awesome to see more & more devs getting interested in math & stats as machine learning, optimization & sequential testing (what you folks call a/b test) becomes more mainstream.
The obvious benefits are, much of math literature is currently written for consumption by math grad students & profs, so all of this will get a much needed rewrite. I foresee a surge in material like Jeremy Kun’s popular “math for programmers” book that so many of you loved on HN. Much of math lit is imo unnecessarily dense & terse. Speaking as a phd student, I really appreciate atleast 1-2 numerical examples with answers, not just plain theory. Maybe if there was a Linear Algebra for programmers text with some code in python to accompany the theorems, it would be a huge hit among applied ML researchers & programmers. Graduate linear algebra is actually quite hard & very few phd students pass the linalg quals in their first attempt ( Grad linear algebra, not basic shit like matrix algebra, linear maps & decompositions). This material should imo be rewritten ( “dumbed down” ) so regular folk can also sample some of the magic.
In my previous work at an adtech corp, I built a whole bidding algorithm straight from Strang’s linear algebra book (the chapter on Lagrange optimizers). It’s still running, 5 years & counting. I once joked if they gave a nickel out of every dollar that algorithm made to Prof Strang, he’d be a multimillionaire.
The Coding The Matrix textbook is exactly the type of book you've described that is tailored for programmers.
I've read only the first few chapters and found it a joy even if a little wordy in some parts.
I took a course in linear algebra when I was in grad school, took a graphics course (which is basically applied linear algebra), watched the 3 blue 1 brown linear algebra series on YouTube and have spent some time looking at machine learning algorithms (which seem to be basically applied linear algebra) and I still don't feel like I have any real intuitive sense of the topic. Whenever I read an algorithm description that includes a matrix multiplication, say, I understand exactly what they did, but I've never looked at a problem and thought to myself, "ah, that's a problem for linear algebra!"
it’s a good undergrad book. also on that page he says lights out game is a “linear algebra problem”. It’s actually not. incidentally I worked on Lights out for my thesis, and my advisor made a whole career out of lights out! That game relates to Fibonacci polynomials, studied in Combinatorics - https://www.unf.edu/~wkloster/fib.html
How would you compare coding the matrix to strang ?
I'm going to be starting out on one of these and coding-the-matrix is very python centric (which is super helpful and practical) versus MATLAB in Strang's book.
this is classic “mistaking the map for the territory”.
whether the code is in python or matlab matters little. the code is just a supplement to the main material which is still math. trying to understand that math by reading code is actively counterproductive and actually quite foolish. my professor gave a symposium talk on this topic, so i will use one of his examples -
At 0 minutes you have 0 dollars. suppose each minute you toss a coin. if it’s heads you make a dollar. if it’s tails you pay a dollar out of your pocket. What are the chances you’ll have $30 in an hour ?
This is the sort of classic problem that can be coded up in python or matlab under 5 minutes. it’s just a for loop & an if statement. It won’t tell you much about the actual probability because that number is so small your code will have to run for a long time to get a meaningful answer. Whereas it’s trivial if you just do the math by hand.
it just happens that i dont want to download and install matlab on my computer (which is company provided) and i'd rather work on python if there is an equally good course.
The obvious benefits are, much of math literature is currently written for consumption by math grad students & profs, so all of this will get a much needed rewrite. I foresee a surge in material like Jeremy Kun’s popular “math for programmers” book that so many of you loved on HN. Much of math lit is imo unnecessarily dense & terse. Speaking as a phd student, I really appreciate atleast 1-2 numerical examples with answers, not just plain theory. Maybe if there was a Linear Algebra for programmers text with some code in python to accompany the theorems, it would be a huge hit among applied ML researchers & programmers. Graduate linear algebra is actually quite hard & very few phd students pass the linalg quals in their first attempt ( Grad linear algebra, not basic shit like matrix algebra, linear maps & decompositions). This material should imo be rewritten ( “dumbed down” ) so regular folk can also sample some of the magic.
In my previous work at an adtech corp, I built a whole bidding algorithm straight from Strang’s linear algebra book (the chapter on Lagrange optimizers). It’s still running, 5 years & counting. I once joked if they gave a nickel out of every dollar that algorithm made to Prof Strang, he’d be a multimillionaire.