In computing various relations in high-dimensional metrics (which is a common practice in machine learning, and other computational applications), Tensors come in handy.
Tensors may sometimes be hard to understand as it is very abstract and has variable intuition depending on the context, for example understanding tensors for physics (understanding big things in the cosmos) or abstract mathematics (to understand logical bindings). That’s why there may be many different explanations. But for now, we are going with bases in high-dimentional vector-spaces
A general explanation of Tensors in liner algebra for computation goes as follows; this explanation is attributed from one great answer I found on the mathematics stackexchage.
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