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>Is math going to become more important for software engineers in general, as AI is adopted in to more and more aspects of the SDLC, even if said engineers aren

by chrsig 3y ago

>Is math going to become more important for software engineers in general, as AI is adopted in to more and more aspects of the SDLC, even if said engineers aren't working directly on the AI systems themselves? I'm definitely not any authority on the matter -- also self taught, but I don't think so. Having a conceptual understanding of the math is probably a really good idea, but I imagine there's quite a bit that can be hidden behind an api that you can essentially say "does xyz by means of magic". I imagine this winds up creating three distinct roles: - Focusing on improving the model's usefulness - Focusing on implementing the model for optimal performance - Focusing on using the model within a larger application I say "creating", but I imagine this is also probably roughly the current status quo.

f38zf5vdt 3y ago
The maths are not actually that complicated. 99% of what pytorch is doing is matrix multiplication and addition with n-dimensional arrays (tensors). For backprop you need a very limited understanding of calculus. [1] Stuff like sigmoid/softmax is also straightforward and you can grok it from a 5 minute youtube video. [2] If something doesn't make sense, just ask ChatGPT to walk you through it. There is a lot of other complicated math that you will need when reading papers because people writing AI papers like to overcomplicate a lot of things that can be represented in 5 lines of pytorch. [1] https://towardsdatascience.com/understanding-backpropagation-algorithm-7bb3aa2f95fd https://towardsdatascience.com/understanding-backpropagation... [2] https://www.youtube.com/watch?v=Qn4Fme1fK-M https://www.youtube.com/watch?v=Qn4Fme1fK-M