April 2017
Intermediate to advanced
320 pages
7h 46m
English
Content preview from Deep Learning with TensorFlow
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Feed-forward and backpropagation
The backpropagation algorithm aims to minimize the error between the current and desired output. Since the network feed-forward, the activation flow always proceeds forward from the input units to the output units. When compared with the output from the one expected, the gradient of the cost function is backpropagated through the modification of weights.
This method is recursive and can be applied to any number of hidden layers.
The backpropagation algorithm processes the information in such a way that the network decreases the global error during the learning iterations; however, this does not guarantee that the global minimum is reached. The presence of the hidden units and the non-linearity of the output ...
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