Very nice! Next thing to add would be numerical gradient testing.

Is that where you approximate a partial derivative as a difference in loss over a small difference in a single parameter's value?

Seems like a great way to verify results, but it has the same downsides as forward mode automatic differentiation since it works in a pretty similar fashion.