Looking at the material here, I think you may be under estimating what is meant by "zero" here. To fully follow these notes you would need to have significant research-level knowledge of general machine learning techniques, with a good deal of specific experience working with neural networks (both in theory and application).

I think someone with a general graduate-level background in most fields of ML or computer science could acquire a working knowledge of the SOTA in a particular associated sub-topic after reading through a half dozen or so research papers and code. That doesn't seem unreasonable or surprising.

He's also under estimating what it means to be a hero. Most of the experiments here are on Cartpole which is a very basic environment (not even MNIST level of complexity). Most papers in RL use Atari, which requires some amount of engineering that this repo does not have.

Understanding and making use of E=mc^2 takes less time than it took to research it.

Hate to be that person, but -

This is not the spirit that some of us want to see in HN. Efforts are always appreciated. The field is improving incrementally, in a very fast way.

come on man, the title is obviously click bait; you can only begin to build an understanding of a field with a single tutorial, even if it's super in depth