What is being meant is that the example is one of "unrolled loops" - that is, to an extent, some of the matrix math has been unrolled in order to show the discr

What is being meant is that the example is one of "unrolled loops" - that is, to an extent, some of the matrix math has been unrolled in order to show the discrete steps involved in the calculations that would be lost (more opaque) for someone learning. Real systems like TensorFlow (and Keras, which is a wrapper that simplifies TensorFlow - which is already pretty simple; if you come to love TensorFlow, you will really love Keras - but you can't really appreciate TensorFlow until you understand the basics of what it does - which the posted tutorial tries to show) hide all of these steps behind a variety of API that tries to mimic the conceptual ideas behind neural networks (ie - "layers" of "neurons" with "connections") while hiding the lower-level "complex mathematics". That's a good thing on one level, because it opens up ANNs for more users. But it's a bad thing at another level, for those who want to know what is going on in those "black boxes", as well as for those who sometimes will stumble into certain issues with their networks, and struggle to understand certain things if they don't understand those underlying systems. No one implements neural network code the way it is shown in this tutorial for much the same reason most people don't code in assembler: Because there are higher level means to work with the same operations and do it quicker, more efficiently, and more correctly (also, the lower level operations used in such libraries have been rigorously tested to be correct - it's similar to the reasoning on how you don't roll your own crypto, because you will likely get it wrong - even if you are an expert in the domain).