My experience so far is that it's important to know WHY you use linear algebra. The idea to me is that almost all input data passed to a machine learning algori

My experience so far is that it's important to know WHY you use linear algebra. The idea to me is that almost all input data passed to a machine learning algorithm should be transformed to a multi-dimensional array of doubles. It's easier to write algorithms against this. Since a lot of ML libraries use native libraries for linear algebra, you might see a lot of implementations that are written in terms of linear algebra operations. So, if you're trying to read the code and you don't understand what the operations do, it may be hard to grok. So, yeah, I think some understanding of linear algebra is necessary. Because it's sort of the atomic set of operations underlying most ML you'll see. To read the code, you need to be able to read the linear algebra. But you probably don't need to go read a book on linear algebra. I tried that and it pulled me away from what I wanted to know. It might be enough to just understand the numpy docs.