I was initially in the field of pure math, but I successfully transitioned to being a researcher of machine learning with much less effort than I expected. My strategy was, instead of learning CS and ML from scratch, to read many interesting recent ML papers and, whenever I encounter with unfamiliar concepts or papers that are cited and seem to contain the information necessary for my understanding, I tried to find the relevant documents online to understand it.
While there is a vast amount of knowledge accumulated in ML, this unstructured way of pursuit strategy of knowledge turned out to be sufficient for me to cover the important information to contribute to the current state of research in a way I like, probably much more efficiently than by learning in a more structured way. I call this "backward learning strategy."
I think this may be similar to how researchers learn when they transition from an area to an area after PhD. Of course, feasibility of this learning must be dependent on the field. Even for pure mathematicians, one cannot understand an arithmetic geometry paper without at least having learned AG in textbook-level. However, it was not difficult for me to understand a paper on a certain kidney disease after reading some relevant papers, without much knowledge in medicine.
Unfortunately, I've not been able to find relevant information about this strategy online. I want some paper references for this learning.
Edit: Asking for anectodes may be not suitable for this site, so I changed it to paper references instead.