[AlexanderNull]

Yeah, if you're not programming your models in binary then gtfo imposters!!

On a more serious note, yes understanding this and anything really takes time and investment. The problem for me at least originally not only with ML but originally with engineering back when I was trying to learn (and couldn't afford school), was finding quality sources for getting started in that process of learning. By providing simplified resources like this google one, the hope is that many beginners can get that one "aha!" moment where they start the basic understanding that allows them to start tinkering and learning.

People without a decent understanding shouldn't be submitting research papers, full on agree there. It's basically a waste of everyone's time and harmful for the field of research as a whole as it dilutes the overall signal to noise ratio. However there's so much space in the ML world that doesn't involve research, not only for fun hobby projects, but also even professionally. Resources like this are critical to reducing the knowledge gap out there between researchers and the programmers in the field that work on little ML projects for doing things like sentiment analysis for their company.

These sub-research projects are mission critical at a lot of companies yet are held up at the majority of non-FAANG companies because there's only one data scientist while the teams of engineers are clueless as to how to assist.