Those are great examples! That’s exactly the sort of thing I see the tools currently associated with neural nets being most useful for long term—applications which aren’t really neural nets at all. Automated differentiation and optimization aren’t specific to neural nets, they’re generic mathematical tools. The neural network community just happens to be the main group developing them.
I really look forward to the day when I can bust out a standard DE solver, use it to estimate the frequency of some stable nonlinear oscillator, and then compute the sensitivity of that frequency to each of the DE’s parameters with an extra two lines of code.
Those are great examples! That’s exactly the sort of thing I see the tools currently associated with neural nets being most useful for long term—applications which aren’t really neural nets at all. Automated differentiation and optimization aren’t specific to neural nets, they’re generic mathematical tools. The neural network community just happens to be the main group developing them.
I really look forward to the day when I can bust out a standard DE solver, use it to estimate the frequency of some stable nonlinear oscillator, and then compute the sensitivity of that frequency to each of the DE’s parameters with an extra two lines of code.