Did:
-implemented feedforward() -- the method is pretty condensed, and may need some explaining. I commented it well, though. Even so, there are a lot of data types at play. The method returns the index of the output neuron which had the largest neuron value.
-Implemented a DataPoint class. This class holds the features and the output of a data point. Pretty self-explanatory.
-implemented an abstract Inputter -- the only thing not really self-explanatory is the possibleClasses list. This list holds the possible classes per dataset. The indeces of these classes  in the list are used in conjunction with the index of the highest-scoring output neuron.
-found a dataset -- tic-tac-toe. Binary classifier with 9 features. Seems like a good choice.
-tested network. All seems good.

To do:
-Implement GD. Nearly there. I just need a break first.
-build confusion matrix stuff (precision and recall too)