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Well, there's the notational ambiguity! In maths notation, Σ(x * y)^2 would mean Σ((x * y)^2), but in most programming notation, treating Σ as a function, it w
by mkl 6y ago
Well, there's the notational ambiguity!
In maths notation, Σ(x * y)^2 would mean Σ((x * y)^2), but in most programming notation, treating Σ as a function, it would be as you say. I'm going with the original formula in https://news.ycombinator.com/item?id=23508661 https://news.ycombinator.com/item?id=23508661.
I don't know PyTorch, but regular Python, Numpy, Sympy, etc. seem very similar to Julia in this instance.
- dunefox 6y agoYou're right, that was my mistake - but I wouldn't say that it's extremely ambiguous in this case, I just wasn't consequent enough in reading the formula. > I don't know PyTorch, but regular Python, Numpy, Sympy, etc. seem very similar to Julia in this instance. If you need PyTorch to record the operations for the backward pass later on I believe you need to use PyTorch versions of *, +, etc.: torch.mul, torch.sub, torch.add, etc. In Julia you can just use built in functions and let Flux handle the backward pass.