### How much math do you need to know to be a machine learning engineer?

Is math a foundational skill for AI? It’s always nice to know more math! But there’s so much to learn that, realistically, it’s necessary to prioritize. Here’s how you might go about strengthening your math background.

To figure out what’s important to know, I find it useful to ask what you need to know to make the decisions required for the work you want to do. At Deeplearning.AI, we frequently ask, “What does someone need to know to accomplish their goals?” The goal might be building a machine learning model, architecting a system, or passing a job interview.

Understanding the math behind algorithms you use is often helpful, since it enables you to debug them. But the depth of knowledge that’s useful changes over time. As machine learning techniques mature and become more reliable and turnkey, they require less debugging, and a shallower understanding of the math involved may be sufficient to make them work.

For instance, in an earlier era of machine learning, linear algebra libraries for solving linear systems of equations (for linear regression) were immature. I had to understand how these libraries worked so I could choose among different libraries and avoid numerical round off pitfalls. But this became less important as numerical linear algebra libraries matured.

Deep learning is still an emerging technology, so when you train a neural network and the optimization algorithm struggles to converge, understanding the math behind __gradient descent__,__ momentum__, and the __Adam__ optimization algorithm will help you make better decisions. Similarly, if your neural network does something funny — say, it makes bad predictions on images of a certain resolution, but not others — understanding the math behind neural network architectures puts you in a better position to figure out what to do.

Of course, I also encourage learning driven by curiosity. If something interests you, go ahead and learn it regardless of how useful it might turn out to be! Maybe this will lead to a creative spark or technical breakthrough.