We really need to name tensors differently. "Array" would be a more fitting name.
Tensors are really different mathematical objects with a far more rich structure than those used in the Deep Learning context.
Tensors are really different mathematical objects with a far more rich structure than those used in the Deep Learning context.
https://en.wikipedia.org/wiki/Vector_(mathematics_and_physic...
In linear algebra you should learn this as soon as you hear about it. Is the english Wikipedia page correct in that you always use nabla for the gradient? https://en.wikipedia.org/wiki/Gradient#Generalizations
Because `grad f = ∇f` holds in scalar fields f only (not in vector or tensor fields (tensors that aren't scalars)).
In the ML context it generally doesn't matter because you don't do these transformations (and more generally there is no metric for your space). So you can just treat a gradient as an array of numbers. But in a physical context the distinction starts to matter, at least on a manifold with curvature.
The one place it does matter in ML (that I'm aware of) is information geometry, where you try to do a transformation from the normal coordinate space to a more "natural" coordinate system that is based on the Fisher information, and this introduces curvature into model.
Just calling it an array might be underselling it, at this point. Perhaps a tensor in the context of CompSci is just a particular type of array with certain expected properties.
I like the NDArray terminology used by numpy. I think it gets the point across more clearly than “tensor”, and conceptually they’re pretty much equivalent
Would indeed be cool to have true tensor processing units :-)
PKD level of cool as you could argue those chips directly process spacetime chunks