Say I trained a Neural Network (not RNN or CNN) to classify a particular data set.
So I train using a specific data set & then I test using another and get an accuracy of 95% which is good enough.
I then deploy this model in a production level environment where it will then be processing real world data.
My question is, will this trained NN be constantly learning even in a production scenario? I can't figure out how it will because say it processes a dataset such as this:
[ [1,2,3] ] and gets an output of
[ 0, 0.999, 0 ]
In a training scenario it will compare the predicted output to the actual output and back propagate but in a real world scenario it will not know the actual value.
So how does a trained model learn in a real world scenario?
I am still very much a beginner in this field and I am not sure if the technology used is going to affect the answer to this question, but I am hoping to use Eclipse Deeplearning4J to create a NN. That being said the answer does not need to be restricted to this technology in particular as I am hoping more for the theory behind it and how it works.