Or as the subspace of all the vectors are orthogonal to a given single vector, or as the subspace generated by any orthogonal basis with one base vector removed, or as the kernel of a linear form, ... – but a more visual explanation is probably better as a first foray in the question.
I agree that a more visual explanation is better in general.
I was trying to hint how the visual explanation relates to the long vectors of numbers we actually feed our machine learning contraptions with. Not sure I was successful.
Or as the subspace of all the vectors are orthogonal to a given single vector, or as the subspace generated by any orthogonal basis with one base vector removed, or as the kernel of a linear form, ... – but a more visual explanation is probably better as a first foray in the question.
I agree that a more visual explanation is better in general.
I was trying to hint how the visual explanation relates to the long vectors of numbers we actually feed our machine learning contraptions with. Not sure I was successful.
This is really good !