81–90 of 119 posts
In the author's example, the function max(0, x) they subsequently differentiate isn't differentiable.
Earlier quoted context omitted.
You don't need to do a second bachelors - you really need four or so courses. If you have the patience and dedication you can sit down with the textbooks and work through them on your own.
Can you recommend books and online courses to hammer these concepts down? I used PCA and k-means for my masters thesis but didn’t really know how well they work under the covers.
Anyone have a suggestion for a good online course in linear algebra?
Yes, UIUC offers very good online math courses: https://netmath.illinois.edu/college/math-415 . There is also a more pure/abstract version of that course available. If you don’t care about accreditation and are patient, sit down with Axler’s Linear Algebra Done Right and Hoffman & Kunze’s Linear Algebra , in that order. I would caution you against trying to learn linear algebra using a “take what you need” approach.…
Anyone have a suggestion for a good online course in linear algebra?
The video lectures of Prof. Gilbert Strang’s linear algebra class at MIT are very good: http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-... He's an amazing teacher and conveys a lot of intuition + makes even complicated ideas look straightforward.
Anyone have a suggestion for a good online course in linear algebra?
I like "Coding the Matrix" by Philip Klein of Brown delivered via Coursera. It's a deep content intro to linear algebra (and more), with a focus on applications in computer science. The course is accompanied by a textbook written by Klein, which makes the course material better organized and more in-depth than slides and videos alone would allow. http://codingthematrix.com/
Earlier quoted context omitted.
Awesome. I had a really difficult time with math in HS, and never pursued it at all in college, so even though I'm a programmer my math skills are barely at a high school level. I'd love to get into ML but the math keeps me at bay.
We should find or start a Slack / Discord where we go through a math textbook and conquer our fear of mathematics together.
I think a lot of people need to start from the basics because they don't have a good foundation in math. The core problem is schools will push you along if you can somehow produce the correct answer for 70% of the problems on a test. Combine this with intense pressure not to fail and you will very likely end up in higher level math courses with many gaping holes in your foundational knowledge. You thus end up relying…
> Like, I didn't understand how division works -- if someone were to ask me what (3/4) / (5/6) even means conceptually I would not have been able to provide a coherent, accurate explanation. "Uh... it's like taking 5/6 of 3/4... wait no that's multiplication... you need to flip the second fraction over... for some reason..." In case you (or others reading this) still struggle to formalize division, a very nice way to…
My bullet list, which might be too ambitious and theory-focused, but this is what I used from my physics background. Learn some: Calc up to 3 (you can skip some of the divergence and curl stuff) Linear algebra (no need for Jordan change of basis) Real analysis Intermediate probability theory (MAE, MAP, conjugate priors minus the measure theory stuff) A little bit of differential geometry (at least geodesics. This is…
Can you give any recommendations for a little bit of differential geometry?
If you have a background in physics then some combination of Nakahara's 'Geometry, Topology and Physics' and Baez and Muniain's 'Gauge Fields, Knots and Gravity' might be good (I haven't included relativity textbooks as I assume it you have a background in GR then you have enough differential geometry).
An unusual recommendation that I think is really nice is 'Stochastic Models, Information Theory and Lie Groups' by Chirikjian. It covers a few other topics mentioned in this thread and is really nice. It's _extremely_ concrete and spells out a lot of calculations in great detail. Plus, the connection to engineering applications is much more obvious.
Earlier quoted context omitted.
> 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. To play devil's advocate, (EDIT: an intuitive understanding of) probabilistic reasoning (probability theory, stochas…
> To play devil's advocate, probabilistic reasoning (probability theory, stochastic processes, Bayesian reasoning, graphical models, variational inference) might be equally if not more important. And of course, you're not going to get very far with probability theory and stochastic processes unless you have a mature understanding of analysis and measure theory :) This comment exchange neatly demonstrates the intrinsi…
"comfort" is a perfectly cromulent word for this.