use OpenMP to parallelize learning

[redpony]

During learning, computing the loss and its gradient relative to the parameters (especially with large numbers of training instances or features) can be quite expensive. OpenMP (http://openmp.org/wp/), which is supported by default with g++, could easily be used to parallelize this computation. Basically, all the loops of the following form
for (unsigned i = 0; i < training.size(); ++i)
are good candidates for parallelization. Reading about OpenMP such "reductions" will have to be implemented by creating a gradients buffer per thread and then summing them at the end (although this summing could also be parallelized).