I wanted to compare some (pre-existing) Python code I have for Radial basis function (neural) networks (RBFNN) for the goal of function approximation, with what I could obtain with Mathematica 11.0.
I found some old documentation that indicates mma used to have RBFNNs implemented in a "straight-forward" fashion.
However, I can't find any mention of them in the mma v. 11 documentation; I'm pretty much a neophyte in neural networks, but it seems that in the
Neural Networks package of the current version of mma NNs can be implemented in a "Lego"-like fashion by stacking together layers of different kinds (if the analogy makes sense).
So my question is, is it straightforward to implement RBFNNs in mma 11 with the its
Neural Network package (and if so, can someone show how)? Or would I be better off ignoring the
Neural Network package completely and writing/porting an implementation based on "first principles"?