Re:The Matrix Calculus You Need for Deep Learning
by mrfox321 on Monday July 31, 2023 @01:37PM
(#36946113)
Re [1]: it's fairly concrete to simply say that matrix multiplication can be performed block-wise.
Re:The Matrix Calculus You Need for Deep Learning
by PartiallyTyped on Monday July 31, 2023 @02:25PM
(#36946777)
I don’t disagree; but that is just an example of MM. The gist is not that you can do block multiplication; but that you can define matrices over any valid ring, which includes other matrices - ie blocks.
Re:The Matrix Calculus You Need for Deep Learning
by mrfox321 on Monday July 31, 2023 @03:20PM
(#36947501)
Yeah matrices are more abstract. I guess I am just pointing out that your concrete example of non-commutative rings (matrices of matrices) still needs a proof to demonstrate bijection between 4N x 4N (scalar) and 4 x 4 (N x N(scalar)).
Block MM demonstrates the equivalence.
Re:The Matrix Calculus You Need for Deep Learning
by tikhonj on Monday July 31, 2023 @08:13PM
(#36950671)
You can even generalize linear algebra algorithms to closed semirings and have some really cool algorithms pop out, like finding the shortest path in graphs. There's a great paper called "Fun with Semirings" that goes into more details; unfortunately looks like the PDF isn't easily available online any more, but I found some slides[1] that seem to cover the same ideas well enough.
[1]: https://pdfs.semanticscholar.org/2e43/477e26a54b2d1a046c2140...
Re:The Matrix Calculus You Need for Deep Learning
by PartiallyTyped on Tuesday August 01, 2023 @02:39AM
(#36953053)
This deserves its own HN post imho.
Re:The Matrix Calculus You Need for Deep Learning
by PartiallyTyped on Tuesday August 01, 2023 @06:47AM
(#36954495)
Okay I went over the slides and good lord this would have made my life easier not too long ago.
Matrices themselves form non-commutative rings too; and based on this, you can think of a 4N x 4N matrix as a 4x4 matrix whose elements are NxN matrices [1] :D
[1] https://youtu.be/FX4C-JpTFgY?list=PL49CF3715CB9EF31D&t=1107
You already know whose lecture it is :D
I love math.. I should have become a mathematician ...