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Home / Blog / Data Science Digital Book / Multi-Layered Perceptron (MLP) / Artificial Neural Network (ANN)
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The nonlinear pattern will not be captured by the mere existence of hidden layers. The activation function that will be employed must not be linear.
Only linear patterns may be captured by using linear or identifiable activation functions inside the neurons of the hidden layer.
A linear activation function is assumed by default by the network if no activation functions are provided in the layers.
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L1 regularization / L1 weight decay term
L2 regularization / L2 weight decay term
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This method of model averaging in the Deep Learning Training Phase is interesting: Ignore (zero out) a random subset, p, of nodes (and associated activations) for each hidden layer, training sample, and iteration.
In the test phase, use all activations, but scale them down by a factor p (to make up for the activations that were not present during training).
Choose a selection of nodes at random, then reduce their output to zero.
Randomly select a subset of nodes and force their output to zero.
But unlike dropout, we deactivate the weights rather than the nodes. The nodes are a little bit active here.
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Input: Values of x over a mini-batch: B = { x1...m };
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