"Math Basics" It's 2000 pages long....
Chapter 1 can't be more basic, a great summary of what will remain intuitively after reading the whole opus
Math Basics for Computer Science and Machine Learning [pdf]
71–80 of 122 posts
Re: Math Basics for Computer Science and Machine Learning [pdf]
#72Q: Is this all necessary for ML?
Re: Math Basics for Computer Science and Machine Learning [pdf]
#73Q: Is this all necessary for ML?
No, you can be an ML practitioner with just an intuitive understanding of, say, gradient descent works and you would do fine. You can even pick up that intuitive understanding on a strictly need-to-know basis, when it's needed for learning an ML technique. That's what fast.ai teaches.
For being more than a practitioner, like an implementer of new ML libraries or a researcher, of course you'd need to know more.
Re: Math Basics for Computer Science and Machine Learning [pdf]
#74Great book. I think it's a good time to mention a couple of nice books (related) 1. Elementary intro to math of machine learning [0]. Its style is a bit less austere than that of OP's. It also has a chapter on probability. It could possible serve as a great prequel to the book linked in the OP. 2. The book on probability related topics of general data science: high-dimensional geometry, random walks, Markov chains, r…
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Re: Math Basics for Computer Science and Machine Learning [pdf]
#75The professor who wrote this is Jean Gallier, and I had him for advanced linear algebra at Penn. I am also pretty close to him in so far as a student can be close to a professor. On a personal note, he is one of the funniest professor I've had, and all math professors are characters.
For the people who are interested in ML, the thing to remember here is that he is a Serious mathematician, and he values rigor and in-depth understanding above all. A lot of his three star homework problems were basically impossible. He writes books first and foremost so he can understand things better. In math books, there's the book you first read when you don't understand something, then the book you read when you understand everything. This is book in the link.
for linear algebra, this:https://www.amazon.com/Introduction-Linear-Algebra-Gilbert-S...)
Re: Math Basics for Computer Science and Machine Learning [pdf]
#76In basic calculus one can burn countless hours memorizing mechanical rules to derive and integrate different function forms, or one can just plug the function into something like wolfram-alpha and get, for a lot of useful cases, a symbolic answer, or at least some approximate answer for a point or interval. The point is, understanding integrals and derivatives doesn't require one to memorize all the mechanical rules.…
> No one should go with pen an paper double checking if that polynomial integral is correct or not!
Hm - maybe I'm fortunate that I studied calculus before there were (accessible) software packages that could just do this stuff for you, because back then, the only way to solve these was to do them on paper. I'm sure I would have been tempted to just "skip ahead" to letting the computer do it for me, but I definitely learned a lot more going through all of the steps myself than I would have if I had just gotten a high-level understanding of what was going on and plugged the rest into a computer. Because, honestly, integrating polynomials is really, really easy - if you know how to do it, you can do it on paper faster than you can load up wolfram-alpha, type it in, and wait for an answer.
Re: Math Basics for Computer Science and Machine Learning [pdf]
#77In basic calculus one can burn countless hours memorizing mechanical rules to derive and integrate different function forms, or one can just plug the function into something like wolfram-alpha and get, for a lot of useful cases, a symbolic answer, or at least some approximate answer for a point or interval. The point is, understanding integrals and derivatives doesn't require one to memorize all the mechanical rules.…
This one does not. it's proof heavy, and there isn't really a mechanical way to do proofs that's efficient. The understanding comes first and often all in bunches when "the light turns on," then the proof follows.
Re: Math Basics for Computer Science and Machine Learning [pdf]
#78Earlier quoted context omitted.
+ 100. I can't upvote this enough. Programmers have spent countless hours practising programming to the point where they have forgotten how difficult it was in the beginning. A non programmer might think of programming as "memorizing hundreds of rules" to get anything done, but one doesn't learn programming by sitting around explicitly memorizing hundreds of rules and then begin to program. Actually writing programs…
I've been teaching programming for five or six years now. I always start with HTML, then add CSS, and then add JavaScript. That way they experience mastery all the way, and see how they can be creative with the code. It's so great to see a pupil "get it" -- and sometimes even pupils that "suck at math" or even pupils who have problems spelling the most basic sentence correctly. In fact I've found that there's a stran…
> I always start with HTML, then add CSS, and then add JavaScript
So when do you get around to teaching programming, then? ; P
/ducks
Re: Math Basics for Computer Science and Machine Learning [pdf]
#79In basic calculus one can burn countless hours memorizing mechanical rules to derive and integrate different function forms, or one can just plug the function into something like wolfram-alpha and get, for a lot of useful cases, a symbolic answer, or at least some approximate answer for a point or interval. The point is, understanding integrals and derivatives doesn't require one to memorize all the mechanical rules.…
You don't need to memorize rules when studying math. Just like you don't need to spend any time to memorize syntax for programming languages. You automatically remember things you use a lot. Once you have spent countless hours doing exercises to the extent that you understand the math, you already remember the rules. If you have not spent countless hours doing exercises, you don't understand anything at this level. Y…
I'm coaching my son through high school math. One of the things I'm trying to impress upon him is focusing on understanding _why_ these formulas work rather than just memorizing the formulas themselves - if you understand why they work, you can always re-derive them if you need to, and you may forget the details of what they do, but you'll never forget the details of why they work once you understand them.
Re: Math Basics for Computer Science and Machine Learning [pdf]
#80From the start of Chapter 2: "In the following four chapters, the basic algebraic structures (groups, rings, fields, vectorspaces) are reviewed, with a major emphasis on vector spaces. Basic notions of linear algebra such as vector spaces, subspaces, linear combinations, linear independence, [...], dual spaces,hyperplanes, transpose of a linear maps, are reviewed." If anyone needs to start even earlier than this, I'v…
Not to look a gift horse in the mouth, but I'm always irritated by math books that include practice exercises but no answers in the back of the book to check your work against.