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>...but if you problem is, given an input of say [price, date, origin] and an output of [value to business], you can solve that problem quite trivially without
by axiom92 9y ago
>...but if you problem is, given an input of say [price, date, origin] and an output of [value to business], you can solve that problem quite trivially without needing any neural network.
That's one general statement over another. Consider https://arxiv.org/abs/1508.00021 https://arxiv.org/abs/1508.00021 as a counterexample.
- shadowmint 9y agoYes it is; and you are correct, it's not necessarily trivial; but the point I'm making isn't about reducing very high dimension inputs into lower dimension outputs or picking the best function to map between data sets. ...how you implement those functions, how you optimise for the best results and performance is an implementation detail. If you are picking your solution before you know what your problem is, you are falling into premature optimisation. Understanding how to reduce your problem domain into a series of functions that take numerical data and return numeric data is the key practical problem that most people face, and it's poorly described in the literature. (That's why most people just pull out that classic line '...its great for image recognition and ...many other things...')
- rs86 9y agoIt is an implementation detail if the current implementation is viable, but if it is not, then designing a viable one might be critical