The problem with that is these kinds of simplified "how it works" neural net examples don't scale for real world problems - especially when using an interpreted

The problem with that is these kinds of simplified "how it works" neural net examples don't scale for real world problems - especially when using an interpreted language like Python. Even a simple but useful scenario like the MNIST number recognition system would probably run "dog-slow" on such a neural network, given the number of input nodes, plus the size of the hidden layer - the combinatorial "explosion" of edges in the graph, all the processing needed during backprop and forwardprop... It might be doable with C/C++ - but it still won't run as well as it could. These examples are really just meant as teaching tools; they are the bare-bones-basics of neural networks, to get you to understand the underlying mechanisms at work (actually, the XOR network is the real bare-bones NN example, because it requires so few nodes, that it can be worked out by pencil and paper methods). Real-world implementations are best done using more advanced libraries and tools, like Tensorflow and Keras, or similar (and moving to C/C++ and/or CUDA if even more processing power is needed).