Okay so, I understand that inputs are sent directly to the network (basically being multiplied to weights of the nodes, receive a bias, and gets activated) and then the network produces an output through this feed forward method.
Now, in order for the network to "learn," we determine the error by comparing the predicted output versus the "known output / answer," and then back-propagate. Right?
What I don't get it is if the output goes through some activation function, say Sigmoid (results to a value between 0.0 - 1.0), how can it ever learn if the known output is a continuous value such as "Salary", "Number of Something", or in the use-case I have, "Volume of Orders" which ranges from anywhere between 300 to 1500.
// Example I have 7 inputs // They go through a hidden layer that consists 16 hidden neurons // and my output is a single neuron that's supposed to be "Volume of Orders" // // MyInputLayers --> HiddenLayers --> Output=0.123 // // Since I learn from tutorials that Output layers go through an activation, // I think it's impossible for the network to learn because a // prediction of 0.123 compared to the "known output" that is, say 615 // is almost ridiculous
Should I apply Sigmoid to the "known output" as well? If so, how can I remap it when the network has already learned and is ready to do some predictive magic?
I must be missing / not understanding something here. Thank you in advance.
p.s. My dataset is composed of 6 months of records; an Excel file that has 200,000+ rows and 8 columns (7 leading, 1 lagging).