Create notebook to compare gradient and meta-heuristic MLP training

[jairodelgado]

We need to create a notebook to compare gradient based training of MLP networks with meta-heuristic training. We suggest to proceed as follows:

1. Use the datasets provided by Include small-size datasets for benchmarking #11 to perform the comparison.
2. Use a fixed sized architecture for the MLP (for example, single hidden layer with 10 neurons using logistic activation function).
3. Consider meta-heuristic algorithms PSO, FA and CS. Hyper-parameter configuration of such algorithms should be archived by using Random Search.
4. Consider the gradient based algorithms: SGD and ADAM provided by skit-learn. Hyper-parameter configuration of such algorithms should be archived by using Random Search.
5. Take a look to hyper-opt to perform hyper-parameter configuration.

The output of this issue should be a new folder within docs directory.

• Name the new directory as: docs/bench-accuracy
• Include within this folder the mlp-sgd-vs-meta.ipynb and mlp-sgd-vs-meta.html notebook.