(Or "backpropagation") A learning algorithm for modifying a feed-forward neural
network which minimises a continuous "error function" or "objective function."
Back-propagation is a "gradient descent" method of training in that it uses
gradient information to modify the network weights to decrease the value of the
error function on subsequent tests of the inputs. Other gradient-based methods
from numerical analysis can be used to train networks more efficiently.
Back-propagation makes use of a mathematical trick when the network is simulated
on a digital computer, yielding in just two traversals of the network (once
forward, and once back) both the difference between the desired and actual
output, and the derivatives of this difference with respect to the connection
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