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.

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 bookâlike 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?

No bullshit guide to linear algebraby Ivan Savov.https://www.amazon.com/No-bullshit-guide-linear-algebra/dp/0...

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...

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

> 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

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 bookâlike 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

doyou suggest?[1] https://www.amazon.com/No-bullshit-guide-linear-algebra/dp/0...

Shameless plug, check out my book https://www.amazon.com/dp/0992001021/noBSLA for an in-depth view of the linear algebra background necessary for quantum computing.

If you know linear algebra well, then quantum mechanics and quantum computing is nothing fancy: just an area of applications (See Chapter 9 on QM). Here is an excerpt: https://minireference.com/static/excerpts/noBSguide2LA_previ...