Training configuration JSON file I/O and new examples

New in this release

1. The app/train-cloud-microphysics.f90 program reads hyperparameters and network configuration from the new training_configuration.json input file and defines the corresponding variables in the program.
2. The new example/print-training-configuration.f90 program displays a sample input file as shown below.
3. The new example/learn-microphysics-procedures.f90 program learns to model two functions from [ICAR]'s Thompson cloud microphysics model.
4. Updated netcdf-interfaces dependency.

./build/run-fpm.sh run --example print-training-configuration 
Project is up to date
 {
     "hyperparameters": {
         "mini-batches" : 10,
         "learning rate" : 1.50000000,
         "optimizer" : "adam"
     }
 ,
     "network configuration": {
         "skip connections" : false,
         "nodes per layer" : [2,72,2],
         "activation function" : "sigmoid"
     }
 }

What's Changed

• Cleanup examples by @rouson in #91
• Train neural net proxy for two functions from ICAR's Thompson microphysics model by @rouson in #92
• JSON-formatted input for training configuration by @rouson in #94
• doc(README) add training configuration material by @rouson in #95
• App reads training configuration JSON file by @rouson in #96
• fix(example): work around associate issues by @rouson in #97

Full Changelog: 0.8.0...0.9.0