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The most common form of neural network dropout randomly selects half of a neural network's hidden nodes, at each training iteration, and programmatically drops them -- it's as if the nodes do not ...
In many scenarios, using L1 regularization drives some neural network weights to 0, leading to a sparse network. Using L2 regularization often drives all weights to small values, but few weights ...
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