How to quantify a tokenized user agent string for a neural network?
iOS, Windows and Android should not be viewed as strings but as categories. Categories should be one-hot encoded, and should not share the same input as they are not related to each other.

So let's assume we work with two operating systems: iOS and Windows.

So for iOS, input is: 1 0 and for Windows input is: 0 1.

But now we want to add another OS: Android.

iOS becomes: 1 0 0, Windows 0 1 0, Android: 0 0 1. Note that by adding android, you do not actually change the input! You only add an extra 0 for iOS and Windows, and 0's don't get computed in neural networks 0*weight = 0. 

So by adding an OS, you do not affect what the network has learned for the previously trained OS's.