# No bullshit guide to linear algebra

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If they have showed any interest in math and/or physics, you should consider getting them my MATH&PHYS book: https://www.amazon.com/dp/0992001005/noBSmathphys it's very popular for programmers.

I also have a book on linear algebra, which would be good for people doing more machine learning or data sciency stuff: https://www.amazon.com/dp/0992001021/noBSLA

Both books are perfect for math haters, since they start out with a review of high school math.

Disclaimer: I'm the author of these books.

I have a book that might be of interest to you and/or your girlfriend as a review of math fundamentals like high school math (with exercises): https://nobsmath.com/ see also free concept maps https://minireference.com/static/conceptmaps/math_concepts.p... and book preview https://www.amazon.de/dp/0992001005/ and https://www.amazon.de/dp/0992001021/

Good luck with re-learning math topics... it's very cool stuff. A good source of knowledge buzz ;)

There is the No bullshit guide to linear algebra by Ivan Savov.

https://www.amazon.com/No-bullshit-guide-linear-algebra/dp/0...

Here is a shameless plug to my book on linear algebra that comes with an introduction to quantum mechanics (Chapter 9): https://www.amazon.com/dp/0992001021/noBSLA

If you know you linear algebra well, learning quantum mechanics is not so complicated, see the book preview here: https://minireference.com/static/excerpts/noBSguide2LA_previ...

Speaking of learning/reviewing linear algebra, I wrote the NO BULLSHIT guide to LINEAR ALGEBRA which covers all the material from first year in a very concise manner.

preview: https://minireference.com/static/excerpts/noBSguide2LA_previ... condensed 4 page tutorial: https://minireference.com/static/tutorials/linear_algebra_in... reviews on amazon: https://www.amazon.com/dp/0992001021/noBSLA

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, 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://www.amazon.com/dp/0992001021/noBSLA#customerReviews

"Pay the authors" is a really good strategy to incentivize the production of quality content. Get rid of the publishers and just have a short supply chain: author --print_on_demand--> readers. With a price tag in the 20-50 range, a prof could make a living from this book, even if the book isn't popular. When using print-on-demand and cutting out all the middlemen, the margins are very good (50% of list price vs 5% if going with mainstream publisher).

The useful part of a publisher is developmental editing (product) and copy editing (Q/A), so there is an opportunity for "lightweight" publishing companies that help expert authors produce the bookâlike self publishing, but you don't have to do the boring parts. I'm working in that space. We have two textbooks out: https://www.amazon.com/dp/0992001005/noBSmathphys and https://www.amazon.com/dp/0992001021/noBSLA

I'm curious what you think of the "No Bullshit Guide to Linear Algebra" [1]? I'm considering buying it to refresh my knowledge from school. Or what books do you suggest?