ENH + NEW: Implemented the data normalization funcionality to DataSet. Created a new class NormalizationStrategy.

ENH: Only classes belonging to API are now contained in 4neuro.h. All those classes were enclosed in 'lib4neuro' namespace.

ADD: added preliminary version of gradient calculation vie backpropagation, added supporting methods to error classes and learning algorithms utilizing gradient calculation

ENH: Removed both linking and include dependencies from examples => lib4neuro can be used just by linking it and including its header without any external dependencies needed.

FIX: Fixed Boost include dependencies, so the examples don't have to be compiled with Boost include directory.

Updated the structure and functionality of NeuralNEtwork class to account for the changes in connections and neurons.
Updated the logic in DESolver to account for the changes in NeuralNetwork

edited the first ode example to use the new DESolver class

changed IDifferentiable interface to include getter providing neurons with activation function based on derivatives of the parental activation function
added neurons representing 1st and 2nd derivatives of the logistic neuron

-added an example testing multi-network error function

FIX: Fixed missing initialization of 'n_activation_function_parameters' variable in the constructor.

ENH: Moved derivative methods to IDifferentiable class-interface, so that not all activation classes have to implement them now.

- Added preliminary connection classes with neural network class able to evaluate inputs via the feed-forward method.
- Added a test case for validating feed-forward capabilities of our networks

preliminary work on connections, neural networks and neurons with neural networks as activation functions