Actually very much. Think of it from an industry perspective. If you are a software engineer that needs to use a network as a plug in module, then you may not

Actually very much. Think of it from an industry perspective. If you are a software engineer that needs to use a network as a plug in module, then you may not need much understanding of linear algebra. But then you also don't need to know much ML either. It is simply a software engineering job. However things change for anyone who gets their hands dirty or actually builds (even just implements know networks ) from scratch . It is common to find that error caused by transferring a known model to your problem, was because of making key mistakes in how the question was posted mathematically. I am currently implementing Neural nets in my job, and have made many errors because of misunderstanding the mathematical operation of a layer. PGMs are almost entirely math and so are embeddings and matrix factorization models. VAEs and GANs also need a solid grasp of the math behind them. Want to touch your loss function ? -> Math. Want to change optimization methods ? -> Math. The visual designs of almost all neural nets are grounded in math, and this fact makes it vital to be good at it, if one wishes to gain anything beyond a surface level understanding of it.