What is a dropout regularization?
A form of regularization useful in training neural networks.
dropout regularization explained in plain English
A form of regularization useful in training neural networks. Dropout regularization removes a random selection of a fixed number of the units in a network layer for a single gradient step. The more units dropped out, the stronger the regularization. This is analogous to training the network to emulate an exponentially large ensemble of smaller networks. For full details, see Dropout: A Simple Way to Prevent Neural Networks from Overfitting.
Example
Practitioners refer to dropout regularization when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.
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The set of examples used in one training iteration.
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Normalizing the input or output of the activation functions in a hidden layer.
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The number of examples in a batch.
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A probabilistic neural network that accounts for uncertainty in weights and outputs.
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A neural network containing more than one hidden layer.
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