Comment by mathisfun123
19 hours ago
i don't understand who is having trouble reading the dialogue here you or i;
> there is absolutely no sense in which the SVD/PCA decomposition is just a rotation matrix... (hint: scaling is extremely important)
...
> SVD is the decomposition of a matrix into two rotation matrices and a scaling matrix, by definition:
yes that's exactly what i was implying when i said SVD more than just rotation, scaling is also important.
my point, which is my same original point, is that if you think learning about rotation/euler matrices is going to prepare you in any way, shape, or form for ML (vis-a-vis SVD/PCA or RoPE or anything else) you're in for a very rude awakening.
You opened with this:
> I've been in ML for ~5 years in multiple FAANGs and I have never seen a rotation matrix.
Presumably you've used SVD, but you've never seen a rotation matrix. So something is cooked.
Maybe corollary: that FAANG job wasn't that interesting.