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Introduction to Linear Algebra for Applied Machine Learning with Python

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Re: Introduction to Linear Algebra for Applied Machine Learning with Python

#21
post #4

are there resources where linear algebra is discussed in the context of deep learning?

Accompanying the book in the sibling comment is Strang's MIT 18.065 course which is exactly what you're after (it specifically covers linear algebra with a focus on data science and ML): https://ocw.mit.edu/courses/mathematics/18-065-matrix-method...

OCW so it's free(!), including the videos and handouts.

Re: Introduction to Linear Algebra for Applied Machine Learning with Python

#22
post #20

I was scrolling through to see if you explain how you typeset the math (notoriously hard on webpages), but I happened to instead find a small mistake. You write: "Elements of $\mathbb{R}^n$ are sets of real numbers." That is an incorrect definition. - It is inaccurate because n doesn't enter into the definition (and thus all Euclidean spaces would be the same). - You want ordered n-tuples, not sets. If you actually d…

Keep in mind those are just notes I made for myself. I decided to put them out there just in case someone found them useful and to signal my skill... By practicing ML/DS in the last couple of years, I came to realize that what I put there is probably more than what is needed to know for applied ML/DS, and conceptual inaccuracies like those have little to none relevance

Re: Introduction to Linear Algebra for Applied Machine Learning with Python

#23
post #20

I was scrolling through to see if you explain how you typeset the math (notoriously hard on webpages), but I happened to instead find a small mistake. You write: "Elements of $\mathbb{R}^n$ are sets of real numbers." That is an incorrect definition. - It is inaccurate because n doesn't enter into the definition (and thus all Euclidean spaces would be the same). - You want ordered n-tuples, not sets. If you actually d…

Keep in mind those are just notes I made for myself. I decided to put them out there just in case someone found them useful and to signal my skill... By practicing ML/DS in the last couple of years, I came to realize that what I put there is probably more than what is needed to know for applied ML/DS, and conceptual inaccuracies like those have little to none relevance

Well, wrong is wrong :-)

I mean no offense by saying that, and it's human nature to be wrong.

However, this is not a "conceptual inaccuracy". It's wrong. Straight up, old-fashioned wrong. And don't tell me it's of "little to no relevance" that your definition of R^2 does not distinguish between (0,1) and (1,0).

Re: Introduction to Linear Algebra for Applied Machine Learning with Python

#24
post #20

I was scrolling through to see if you explain how you typeset the math (notoriously hard on webpages), but I happened to instead find a small mistake. You write: "Elements of $\mathbb{R}^n$ are sets of real numbers." That is an incorrect definition. - It is inaccurate because n doesn't enter into the definition (and thus all Euclidean spaces would be the same). - You want ordered n-tuples, not sets. If you actually d…

Oh and perhaps more immediately: with this definition of R^n, (x,y) and (y,x) are the same point. No good.

Re: Introduction to Linear Algebra for Applied Machine Learning with Python

#25
I just recently finally bought Strang's introduction to Linear Algebra. The book itself is very dense and, dare I say, scary to a newcomer, but his videos are amazingly clear in combination with the book.

I am in the beginning, but so far, I'd recommend the book. Though, the steep price (almost 100USD over here) makes it a bit difficult to recommend. I might buy Boyd’s and Vandenberghe’s Introduction to Applied Linear Algebra later and write a comparison.

Re: Introduction to Linear Algebra for Applied Machine Learning with Python

#26
post #20

I was scrolling through to see if you explain how you typeset the math (notoriously hard on webpages), but I happened to instead find a small mistake. You write: "Elements of $\mathbb{R}^n$ are sets of real numbers." That is an incorrect definition. - It is inaccurate because n doesn't enter into the definition (and thus all Euclidean spaces would be the same). - You want ordered n-tuples, not sets. If you actually d…

Totally agree with this post...however, to be extremely pedantic (and thus not suitable for the article in question), a tuple in the foundations of mathematics is typically defined as a set. That is, (x,y) := {x,{x,y}}, where the latter is the set containing the element x and the set {x,y}. That is how one goes from axiomatic set theory to define tuples of numbers.

Re: Introduction to Linear Algebra for Applied Machine Learning with Python

#28
post #20

I was scrolling through to see if you explain how you typeset the math (notoriously hard on webpages), but I happened to instead find a small mistake. You write: "Elements of $\mathbb{R}^n$ are sets of real numbers." That is an incorrect definition. - It is inaccurate because n doesn't enter into the definition (and thus all Euclidean spaces would be the same). - You want ordered n-tuples, not sets. If you actually d…

Totally agree with this post...however, to be extremely pedantic (and thus not suitable for the article in question), a tuple in the foundations of mathematics is typically defined as a set. That is, (x,y) := {x,{x,y}}, where the latter is the set containing the element x and the set {x,y}. That is how one goes from axiomatic set theory to define tuples of numbers.

Absolutely! In this sense you are absolutely right, but a reader who knows how to construct such tuples (i.e. fill in the blanks in the article) also in all likelihood knows the content of the article. And I bet readers who don't know that already, will not realize that those are the sets the author means.

After stumbling across the author's twitter where he already complains that people are "being mean on HN", and seeing his responses there, I have some serious doubts about whether he is fit to be teaching people mathematics. I honestly applaud him for writing the article, and don't hold the math mistake against him at all. But the incredible defensiveness when confronted with a small mistake is absurd.

Re: Introduction to Linear Algebra for Applied Machine Learning with Python

#29

I just recently finally bought Strang's introduction to Linear Algebra. The book itself is very dense and, dare I say, scary to a newcomer, but his videos are amazingly clear in combination with the book. I am in the beginning, but so far, I'd recommend the book. Though, the steep price (almost 100USD over here) makes it a bit difficult to recommend. I might buy Boyd’s and Vandenberghe’s Introduction to Applied Linea…

As a heads up, the Boyd book is available free on his website.

http://vmls-book.stanford.edu/

Re: Introduction to Linear Algebra for Applied Machine Learning with Python

#30
post #23

Earlier quoted context omitted.

Keep in mind those are just notes I made for myself. I decided to put them out there just in case someone found them useful and to signal my skill... By practicing ML/DS in the last couple of years, I came to realize that what I put there is probably more than what is needed to know for applied ML/DS, and conceptual inaccuracies like those have little to none relevance

Well, wrong is wrong :-) I mean no offense by saying that, and it's human nature to be wrong. However, this is not a "conceptual inaccuracy". It's wrong . Straight up, old-fashioned wrong. And don't tell me it's of "little to no relevance" that your definition of R^2 does not distinguish between (0,1) and (1,0).

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