Most of the examples I have ever seen use Cartesian vectors and matrix representation to illustrate this idea. That's fine and good, and easy to follow, but not very helpful for more abstract cases. For instance, the set of complex linear functions with the standard complex addition and scalar multiplication operations forms a vector space, and thus (by definition) the functions which are its elements are vectors.
$V = \{ ax + b | a,b \in \mathbb C\}$
Suppose I took two copies $V_1$ and $V_2$ of this vector space and formed their tensor product $W =V_1 \otimes V_2 $; the result ought to be a tensor space of order $(2,0)$, but what would the tensors in it actually look like?
How does one go about "taking the outer product" of linear functions $v_1 \otimes v_2$ for $v_1 \in V_1$ and $v_2 \in V_2$?
Further, how would one construct a dual space $V^*$ for such a vector space if, for instance, one were interested in constructing tensors of rank $(0, n)$ or mixed rank $(n,m)$? Examples using Kronecker delta or matrix transposes for Cartesian vectors are not very much help here! Can anyone give me a little guidance to put me on the right path, please? Many thanks.