Experimental training capability

This is the first release with an experimental capability for training neural networks. The current unit tests verify convergence for single-hidden-layer networks using gradient descent with updates averaged across mini-batches of input/output pairs. Future work will include verifying the training of deep neural networks and introducing stochastic gradient descent.

What's Changed

• CI: install gfortran on macOS by @everythingfunctional in #48
• Encapsulate all output and store unactivated weighted/biased neuron output for training by @rouson in #47
• Add activation-function derivative functions by @rouson in #46
• fix(setup.sh): brew install netcdf-fortran by @rouson in #45
• fix(netcdf-interfaces): patch for fpm v >= 0.8 by @rouson in #49
• Test: add test for single-layer perceptron by @rouson in #50
• Add back-propagation by @rouson in #51
• Export everything via one common module by @rouson in #52
• Add nominally complete training algorithm by @rouson in #53
• doc(README): mention training & additional future work by @rouson in #55
• Group input/output pairs into mini-batches by @rouson in #54
• doc(README): mention experimental training feature by @rouson in #56

New Contributors

• @everythingfunctional made their first contribution in #48

Full Changelog: 0.5.0...0.6.0