I want to incorporate an algebra solver into a neural network. Crazy idea, I know. I'm trying to generate algebra problems that are actually solvable, and the best way I can think to do that would be to have a solver implemented in-network. That's a complex piece of code, thus the interest in differentiable programming.<p>Julia? Python? Swift? Something else?<p>My experience has been that the autogradient in TensorFlow would be way too finicky to implement something complex in. Maybe I'm wrong?
Algebra solver, as in solving algebraic equations numerically? The Julia SciML ecosystem has libraries like DeepEquilibiurmNetworks.jl for building neural networks with nonlinear algebraic equation solving as part of the networks (<a href="https://deepequilibriumnetworks.sciml.ai/stable/manual/nlsolve/" rel="nofollow">https://deepequilibriumnetworks.sciml.ai/stable/manual/nlsol...</a>).