Specify multiple hidden layers in the hiddenLayerSize as an array, and generate the random initial parameters.
Unwrap the parameters into the Theta cell array.
Changed nn_cost parameter vector remapping to remap to the cell array instead.
Forward propagate for any number of layers.
Updated regularization to take theta cell array.
Backpropagation.
A clearer (and more correct) version of rolling nn_params back into Thetas.
Added in new unwrapping to main function.
Made a variable for the default spectra size.
Merge branch 'master' into extraLayers
Merge commit 'ecf192167226ce593e6365c345f372b44519dfd4' into extraLayers

Conflicts:
	neural_network_training.m
Changed the variable from nHidden to nLayers.
Fixed visualizing the first Theta parameters
Changed initializing cell arrays to account for some odd behavior from MATLAB and fmincg
Fixed the unrolling of nnParams.
Updated the unrolling in the main training script.
Corrections to expanded layers in nn_cost
A myriad of corrections and fixes to get the cost function to run in the test case of only one hidden layer.
Set max iterations back to 50 for testing speed.
Updated predict to use the new Theta structure.