Here are some of the differences between the two trainers:
- trainnet accepts a loss function input, which makes it easier to customize the loss if the standard regression loss is not enough.
- trainnet lets you track additional metrics while training.
- trainnet supports the LBFGS solver, which can converge quickly for small problems
- trainnet outputs a dlnetwork, while trainNetwork outputs a SeriesNetwork or DAGNetwork.
- trainNetwork accepts a table as input, while trainnet does not. This might make trainNetwork slightly easier to use when training data is in a table.
