Save yourself some time

There are no shortcuts to mastering anything.

For a long time, I was too comfortable with the idea of learn first, do later. I would spend hours online searching for the best tutorial, the best book, or the right YouTube video, only to realize that I was spending all my time preparing to learn instead of actually learning.

Even after choosing a book or tutorial, it is easy to fall into the illusion that you are on the path to mastery. Most of the time, you are not. Books and tutorials absolutely have their place. They are valuable when you know little or nothing about a subject, and they can give you the fundamentals you need to get started.

The problem is that books and tutorials are designed to be absorbed easily. They are polished on purpose. They are written to make ideas clear, structured, and digestible. Real-world problem solving is nothing like that. Whether in research or industry, you are dealing with messy codebases, vague requirements, confusing documentation, and imperfect papers. Learning to navigate that mess is part of the craft. In fact, it is a skill of its own, and it cannot be developed by staying only within clean courses and well-packaged explanations.

The model of learning I eventually made peace with is simple: learn as you go, and learn as you need.

You do not need to know everything before you begin, and you never will. What matters is learning enough to solve the problem in front of you. That is how real growth happens. If you are trying to fix training instability in a large language model, you do not need to become a world expert in natural language processing before taking action. You need to understand the optimizer being used, the architecture you are working with, and the specific issue in the codebase. Along the way, you may end up learning more than you expected, and that is exactly the point.

There is no perfect learning path. There is no flawless sequence of books, courses, and tutorials that will make the process clean. Computing science and software engineering are messy by nature. That uncertainty is part of the work.

Sometimes I miss the certainty of mathematics, where problems can feel definitive and cleanly solvable. But the problems in AI and technology are more alive. They are messier, yes, but also more interesting.

So if you are looking for a fast way to learn this field, here is the truth: there are no shortcuts. The fastest way is often the slow, messy, frustrating way.

That is the path.

Happy learning.