Thanks, these posts are the only thing I've read that have made sense to me thus far (probably because they mostly confirm my suspicion, that there's not really

Thanks, these posts are the only thing I've read that have made sense to me thus far (probably because they mostly confirm my suspicion, that there's not really a strong distinction between 'symbolic' and 'automatic' differentiation, but there are perceived differences based on whether the computation graph is compiled and optimized, and whether the graph is pretty-printed at some point...). Your Python symbolic differentiator is exactly a mini-Theano (or a mini-any deep learning framework). Each `Function` instance (or `Op` in Theano) defines its output and gradient with respect to its input (given gradient wrt output).