Earlier quoted context omitted.
I am really excited about this library. Tensors are the future; matrices are going to look so old in a few years. In fairness, Theano already knew that. I cannot wait to dig into this and am very relieved that Google's best are still using Python for this endeavour...but...using Python 2.7. I have just recently been persuaded by the community that 3.5 is cost free, and here I have this enormous counterexample. For th…
> Tensors are the future; matrices are going to look so old in a few years. I am honestly curious about this point of view. Is there any example where actual multidimensional tensor have any relevance? What I mostly see around is just standard linear algebra operations on matrices and vectors lifted to higher-dimensional tensors point-wise (for instance applying a certain operation to all 2-dimensional subtensors of…
Equally what is matrix multiplication but a bunch of 1-dimensional dot products applied pointwise? why do we need matrices?
I do get what you're saying, and that part of it is that ML / CS folk just use 'tensor' as a fancy word for a multi-dimensional array, whereas physics folk use the word for a related coordinate-basis-independent geometric concept. But for numerical computing broadcasting simple operations over slices of some big array is really useful thing to be able to do fast and to express concisely.
Numerics libraries which don't bother to generalise arrays beyond rank 1 and 2 always feel rather inelegant and limiting to me. Rank 3+ arrays are often really useful (images, video, sensory data, count data grouped by more than 2 factors, ...), and lots of operations generalise to them in a nice way. Good array programming environments (numpy, torch, APL take advantage of this to provide an elegant general framework for broadcasting of operations without ugly special cases.