Conversion to and from tensors

@expandingman had this example on the forum:

Suppose I have a table that has a DateTime column, a String column and two Float64 columns. I may, for example, need to get this into the form of an Matrix{Float32}. The String may represent categorical data, so it may have to be mapped to integer designations, which may then be converted to floats (in some cases this will be further transformed into a "one hot" representation, but that can usually be achieved fairly easily within the machine learning framework itself). The DateTime might have to be converted to Float32's representing, for instance, the number of seconds past a reference time. After feeding this Matrix into some machine learning, I'll get back a Matrix that I'll need to append to the original dataset in some way.
This gives a rough idea of the most basic problem. Things get way more complicated when you start doing stuff with time series and require rank-3 tensors, but even this most basic case often requires a surprising amount of manual work.

Lets discuss potential solutions for this problem in this issue here.

Start with the linked forum example and the discussion in this issue; no repository files, tests, or entry points are named. Define the desired conversions between tabular columns and matrices or rank-3 tensors, including categorical and DateTime handling, and establish round-trip behavior before implementation.