Describe back-propagation.

Describe back-propagation.



We can calculate the error of the network only at the output units. The hidden units represent latent variables; we cannot observe their true values in the training data and thus, we have nothing to compute their error against. In order to update their weights, we must propagate the network's errors backwards through its layers. We will begin with Output1. Its error is equal to the difference between the true and predicted outputs, multiplied by the partial derivative of the unit's activation. Continue this process all the way to the input variables and then forward propagate the updated weights through the network.

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