Basics: How does a neural network work? (Decision)
first of all: I know that there are similar questions like this. But I wanna know the plain basics. Somehow I do miss something important.
Let's assume I Have some data (x,y) -> z where z can be 0 or 1 and x,y in [0,1].
Now I wanna train a neural network with that data and my desired output should be a boundary or a line or curved line in the x,y space where it splits the zeros from the ones (e.g. male/female or whatever).
So, I wanna have one hidden layer. I guess I somehow understand how to feed the network:
feed it with X = (x,y) to the first layer
Do some computation with weights and bias atc.
Compute an Error or Loss function
train the network with e.g. gradiant descent (i.e. updating the weights, etc)
yeah, now comes my problem :D
What is the output in the end? With a given data set the network tries to reproduce my z values for given x,y, right? So, how do I get a Fit or decision boundary or whatever? What is the stuff that one "plots" in the end in the x,y space?
How do I generate "new data" by the network, or is that not possible?
So, my main question is: What is the output? And how do I handle the output? And what are the steps to get a logistic regression plot in the end (that can be found everywhere in the internet, but they do not say what they plot in the end :D)