First Devlog!
I’m building a neural network visualizer to help you see how the network trains itself. 
Today I finished the math layer of the project such as passing inputs through the network(made up of neurons which decide how strongly to fire based on the inputs), calculating how much weight(How strong a connection is between two neurons) contributed to the error and nudging every weight and bias to reduce the error.
I used NumPy to do this because of speed as it was more efficient at running due to being programmed in C(as well as python) and it had built in matrix math functions.
The hardest part was understanding backpropagation specifically how the error gets calculated backwards through every layer using the chain rule from calculus. I figured it out after I realized that the network was just asking which weights caused the mistake and nudging them in the direction that reduces the error.
Why am I building this? Because you can see more information clearly, and what path and connections the network is using, something a terminal can’t show you. And it just looks cool 
To test it works, I trained it on XOR which stands for exclusive or and returns 1 only when one input is 1. 
Next up: the UI