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Some more feedforward backprobagation network questions

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Hey I've got a couple of feed forward backprobagation network questions. Say you want to train the network to 2 input/desiredOutput vectors. Would you train the network to one of the vectors until the weights stablize, and then do the same for the next set of vectors. Then you repeat the whole process until all the weights have stablized? Also does more neurons in the hidden layer mean the larger range of input/desiredOutput vectors that can be trained into the network? And also what would adding more hidden layers do, would it also allow the larger range of input/desiredOutput vectors to be trained? Thankyou.

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