For simplicity, let's assume we want to solve a regression problem, where we have one independent variable and one dependent variable, which we want to predict. Let's also assume that there is a nonlinear relationship between the independent and dependent variables.
No matter the way we do it, we just need to build a proper curved line based on existing observations, such that the prediction is the best.
I know we can solve this problem with neural networks, but I also know other ways to create such curves. For example:
Something I think would also work (do not know if exists): fitting curve using a series of Fourier sine waves, and so on
My questions are:
Is it true that neural networks are just one of the ways to fit a non-linear curve to the data?
What are the advantages and disadvantages of choosing a neural network over other approaches? (maybe it becomes better when I have many independent variables, and another little guess: maybe the neural network is better in omitting the effect of linear dependent input variables?)