If I have $n$ training observations, $m$ number of features per observation, and my neural network has $x$ neurons in the 1st layer, $y$ neurons in the 2nd layer, and 1 output neuron, what is the runtime of finding the parameters for my neural network? Using backpropagation? Using the best algorithm out there to do so?I've looked over a lot of places on the Internet and can't seem to find anyone who's done a derivation of this. I'm very certain it's lower bounded by $O(mf)$ but I don't know how to incorporate $x$ or $y$. I'm asking this because I have a dataset of a certain size I want to try to apply neural nets to, and I'm trying to find a sense of if my dataset is too big for it.
The runtime of a neural net with given numbers of observations, features, and neurons
algorithms;machine learning;neural networks