I don't think reading this will give you any understanding of what tensorflow is useful for, or how to do it.
The steps of writing a tensorflow program are always like the 'how to draw an owl'; first you define some simple tensors... then just, do the rest of it.
Step 1: Define tensors and inputs. OK!
Step 2: Linear regression. OK! (useless, but sure)
Step 3: Generate high resolution cat videos from a corpus of dog videos. Uh... ????
There are plenty of very good simple introductions to tensorflow.
The tensorflow tutorials themselves try to drop people in 'from the top' with high level practical examples, so we're good on that front too.
What's missing is a middle ground of 'and then do something practical but simple'.
You know what the 'best practice' advice for working with a GAN is?
Find someone else's implementation, copy it, and tweek the hyper parameters, change the input.
This is why I recommend people learn keras, not tensorflow; because it isn't super production ready and practical, but it will let you learn to build and test models easily.
...and if there's one 'programmers guide' to tensorflow, it's exactly that:
You don't just 'build' a tensorflow model; problem solved off you go.
Nope, you're going to be going back and tweaking and changing and randomly trying different stuff over and over again until you stumble into a 'good enough' solution to run with.
...and that solution; it might almost work for some other similar domains... but it probably doesn't generalize. You'll probably have to do the whole thing from scratch again.
Machine learning. Fun times.