Create layers, sigmoid activation nodes, forward prop, backward prop, and Stochastic Gradient Descent implementations. Much of the starter code given via Udacity's Self-driving Car nanodegree. I implemented key pieces for each of the above-mentioned pieces.
- miniflow.py - Implementation of the layers, forward and back prop, stochastic gradient descent.
- nn.py - Creates a layer and tests that stochastic gradient descent works as expected (with gradients calculated via correct back prop)