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Symbolic Differentiation in Machine Learning Models
The notion of symbolic differentiation is basically the kind of differentiation you ran into in calculus class. So it turns out that modern machine learning-based models combine functions that are all reasonably easy to differentiate. And being able to compose that for pretty much all the function in your program, that might let you optimize things that you didn't think you would be able to optimize.