Unfortunately, I do not have a recommendation. I learned everything about learning by reviewing my notes. I take notes in a condense and highly abstract forma

Unfortunately, I do not have a recommendation. I learned everything about learning by reviewing my notes. I take notes in a condense and highly abstract format that is quick to write, yet simultaneously gives something close to a one-to-one map of my mind. This way when I review I can learn better ways to study. How I learned my technique: I do Data Science / Data Analytics. When it comes to programming a Data Scientist can get by not knowing how to write a function. Many are horrid coders, and that's okay because they specialize in research and analytics, not writing code. However, if a DS' day to day is writing code, and it's the most painful part of the day, then would my life be better if I knew how to program flawlessly? In response to this question I took a job as a Software Engineer for a while, doing hard core C++14 at the time. (The company gave me the hardest project they had to incentivize me to go back to doing DS for them.) I had learned C many years ago but when it came to C++ I struggled with making a class. I struggled at knowing what the difference between a copy and a move was. I struggled with multiple inheritance. I struggled with smart pointers. I even struggled with reference collapsing rules. It was painful! In response, I developed the learning technique mentioned above, and decided to learn C++ at an accelerated rate. I learned over 250 concepts in less than a half a year, while being productive in a code base, and not just 250 concepts, but I learned the etymology of every concept. I learned how, why, and when to use them. I became a master at C++ and now it is my favorite language. C++ was a great use of this learning technique, because when I learned something it would come up again maybe weeks or months later. I wanted to know C++ so well I never had to google for anything ever again. Long story short, C++ is a great candidate for meta-learning. That's how I learned what I know. Also, the article mentions how subjects cross domains. This is a key aspect of my learning too. Inspired by Hofstadter, isomorphisms between domains has been a hobby of mine too, and I find both Data Science and Discrete Mathematics to be useful when it comes to the study of intelligence and the study of communication. I highly recommend doing the same. It's not only fun, but it can change your life.