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trendymoniker t1_ivf3lp5 wrote

You absolutely need calc 3 and linear algebra for AI. Backdrop is nothing but partial derivatives + ordered bookkeeping. And matrix math is the computational heart of neural networks.

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WashiBurr t1_ivf17t0 wrote

I found calc 3 to be pretty helpful for my AI course, especially when compared to people who didn't take it.

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chcampb t1_ivf5bjn wrote

Calc 3 for us was multivariable calculus. Which if you don't take that, before AI, you dun goofed.

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uluzg OP t1_ivf8e2k wrote

Ours is Vector Calculus. What's the difference?

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OptimizedGarbage t1_ivfaj2b wrote

They're the same. Calc 3 should be considered as a prerequisite for anything involving neural nets. Trying to understand their behavior without it is like trying to go into physics without learning calc.

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chcampb t1_ivf9mhv wrote

Could be similar. Multivariable I think is a precursor to vector.

For us, we learned vector calculus as needed, such as in electromagnetics.

Calc 1 was derivatives and integrals and related. Calc 2 was derivatives and integrals of transcendant functions (exponent/log, trig functions etc) and time series. Calc 3 was a little bit of all of those things but in multiple dimensions, so partial derivatives, double integrals, and probably some vector stuff.

What we did for the vector calc part of emag was anything required there, up through some of the easier boundary value problems methods. But calc 3 didn't include like, divergence and curl, surface integrals, greens or stokes theorem, etc, where these are needed for specific emag problems.

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