Thanks apetrov - checking out these posts!

Thanks partycoder! Points taken; will make some changes.

Regarding numerical gradients, named it so to differentiate it from analytical gradients, which leverage formulas from calculus. The "numerical" ones are calculated using (f(x+h)-f(x))/h every time.

Python community conventions are not camel case for functions...

forwardAddGate

would be

forward_add_gate

and return is not a function call...

return(max(x,y)) or return(x+y)

would be

return max(x, y) or return x + y

spaces around operators...

x + y not x+y

spaces around function args...

def foo(a, b) not def foo(a,b)

and when calling...

foo(1, 2) not foo(1,2)

https://www.python.org/dev/peps/pep-0008/

Just things to think about when publishing python code for the greater community.

There's a package that verifies PEP8 for you. https://pypi.python.org/pypi/pep8

I see the difference now, thanks for the clarification.

Intuitive explanation of backpropagation from first principles with a simple python implementation

Superlatives in this phrase: intuitive, simple.

These should be ideally determined by the reader, not the author.