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Learning is based on training data, and aims at appropriate weights for the perceptrons in a network.
Direct computation is in the general case not feasible.
An initial random assignment of weights simplifies the learning process that becomes an iterative adjustment process.
In the case of single perceptrons, learning becomes the process of moving hyperplanes around; parametrized over time t:
Wi(t+1) = Wi(t) +
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