February 2018
Intermediate to advanced
450 pages
11h 27m
English
Content preview from Deep Learning By Example
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Step 2 – backpropagation and weight updation
We calculate the total error at the output nodes and propagate these errors back through the network using backpropagation to calculate the gradients. Then, we use an optimization method such as gradient descent to adjust all weights in the network with an aim of reducing the error at the output layer.
Suppose that the new weights associated with the node in consideration are w4, w5, and w6 (after backpropagation and adjusting weights).
If we now feed the same sample as an input to the network, the network should perform better than the initial run since the weights have now been optimized to minimize the error in prediction. The errors at the output nodes now reduce to [0.2, -0.2] as compared ...
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