Found in 6 comments
by jpamata
Coding the Matrix: Linear Algebra through Applications to Computer Science [0]

A hands on introduction to both Python and Linear Algebra using real world cases (ex. you are given a high res image, make a low res version to put on your website so that it could load more quickly).


Original thread
by randcraw
The texts that I hate least (LA and I have a long and rocky relationship):

- Coding the Matrix, Klein This has a strong emphasis on LA's utility in CS, and includes concepts outside traditional LA that enrich the narrative.

- Intro to Linear Algebra, Strang Strang approaches LA from a practical less-theoretical angle, which makes it very sensible if you're an engineer but may not be as suitable if you're a mathematician.

- Linear Algebra, A Modern Intro, Poole This is a solid text that has worked out most of its bugs over the editions.

- Linear Algebra and its Applications, Lay Like Poole, this is also a solid and long running text.

The books by Klein and Strang also benefit from free videos of those courses that are available from Coursera/BrownU and MIT OCW. Klein's is also available on the Kindle.

Original thread
by carlosgg
Starts on Page 2 at the bottom and moves up. Half of this course was taught on Coursera in 2013.

Book on Amazon, Kindle version $3:

Original thread
by mliker
While I did take courses in probability, linear algebra, and lots of calculus, until recently, I forgot most of the probability and all of the linear algebra I learned in school. As for calculus, I only remembered how to take basic derivatives. In any case, I've been spending the past month brushing up on my linear algebra and probability, and it's been a struggle, but now that I'm motivated and under no time pressure to relearn the material, I find it way more fascinating than I did in college. In fact, I skipped tons of my linear algebra classes because I thought the subject was dry and dull. I also rushed through my probability and stats homework just so I could get a good grade on them. I think if you're motivated, and you can do basic math, you should be able to educate yourself in calculus, probability, and linear algebra. It'll be a struggle, but with motivation, you'll be able to pick up the concepts.

for probability and stats:

for linear algebra:

this was my college calculus textbook: I can't comment if it was good or not because by college, I had taken calculus twice so it was all a refresher

best of luck! You sound educated enough (yes, I'm judging from the couple sentences you wrote) that I think you won't have any problems acquiring math knowledge with persistence.

Original thread
by mathaftermath
My favorite LA books are Linear Algebra by Friedberg/Insel[0] which is a combination of Axler style book with more computation oriented one (Terry Tao has a set of lectures based off this book). Another one I like is Modern Intro To LA by Henry Ricardo[1] which implicitly introduced me to Replacement theorem which is really overlooked in a ton of LA books. Again, this book's a rigorous mixture of both theory and computation done very well. If you've never seen higher level math before, there's Linear Algebra: Gateway to Mathematics by Robert Messer[2]. It has tons of commentary about elementary set theory and proof techniques along the way. Whenever someone mentions Axler's book, someone else brings up Treil's book. But there's a third one in the same league/group which is Linear Algebra: An Introduction to Abstract Mathematics by Robert Valenza[3]. Other favorites are Coding the Matrix by Philip Klein[4] for Python aficionados and Linear Algebra Through Geometry by Banchoff/Wermer[5] for those who like geometry.

If you are way beyond all this, you can still pick up new things from Advanced Linear Algebra by Steven Roman[6].








Original thread

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