[x] Add biases
[x] Add class Network
    - To help simplify assembly of a typical network
[x] Add add_delta_weights
[x] Add back-propagation algorithm
[x] Convert calcs implementation to matrix mult
    - Current implementation fails to vectorize
[ ] Figure out how to declare the type Layer on input argument
    - Why can't a class take itself as an argument in its constructor?
[x] Implement random initialization for weights and biases
[x] Convert this list to md checklist
[x] Incorporate learning rate in add_delta_weights
[x] Implement add_delta_biases()
[x] Implement cost functions at every level
[ ] Figure out how to store outputs so they don't need to be recomputed all the time
[x] Remove Node() class
[x] Evaluate cost on all examples every time
[x] Convert forward-prop to matrix mult
[x] Review new cost_func argument to insure it is used comprehensively
[x] Replace existing cost functions with cost_m
[ ] Should I have put the example index first on things like input_values?
[x] The weights are changing. Why not the cost?
    - This turned out to be because I was failing to recompute bulk outputs before recomputing cost
[ ] CategoricalCrossEntropy.cost axis expansion looks fishy when supplied with scalar inputs
[ ] Over-train network by repeating trainings on the same training set
[x] Implement mini-batch gradient descent
[x] Vectorize back-propagation to speed up mini-batch gradient descent
[x] remove tuples from delta_weights_and_biases
[x] Move all utility classes to their own module
[ ] Fix outputs() float64 issue - see dtype unit test 
[x] Modify _deriv_a_wrt_z() to return (n,m) shaped arrays
[x] Omitted biases and weights should be None, not empty
[ ] _deriv_a_wrt_z(), why does it recompute from input_values?
[x] Individual inputs are called attributes. What are outputs called? - Predictions!
[x] Create delta_weights_and_biases type with overloaded operators
[x] Remove duplicate DeltasFunnel.average() and PreparatoryUtils.average_of_bulk_deltas
[x] Strip extraneous member data from DeltaWeightsAndBiases
[ ] Implement learning rate decay
[ ]