If somebody has experience or just tried to compare for himself, can you list the differences? Typical applications, limitations, different output ...
Programmatically, they are the same so I wonder, are there any differences besides the names used?
2 main differences may be outlined. 1.NN can work in 2 modes: learning and production. If convolution is used in learning, this is a completely different story. 2.As to production mode, kernel-based transformations are more general. You can write a sophisticated processing hardly imaginable with NN, but the nets are more natural. If you restrict the solution to this class, probably it will work like human vision.
I never heared of production, but do you mean inference?
No. During learning you prepare the net, that is write something into its associative memory. Then, you stop training and make use of your product. Production system is a standard term in common symbolic programming. They are used for such tasks as parsing. Neural net in fact does something similar, only instead of rules it uses learned associations.
Asked: 2018-04-12 00:52:50 -0600
Seen: 436 times
Last updated: Apr 26 '18
please add some context.