Fine-tuning Stacked AEs

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(Introduction)
(General Strategy)
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Luckily, we already have all the tools necessary to implement fine tuning for stacked autoencoders! In order to compute the gradients for all the layers of the stacked autoencoder in each iteration, we use the [[Backpropagation Algorithm]], as discussed in the sparse autoencoder section. As the backpropagation algorithm can be extended to apply for an arbitrary number of layers, we can actually use this algorithm on a stacked autoencoder of arbitrary depth.
Luckily, we already have all the tools necessary to implement fine tuning for stacked autoencoders! In order to compute the gradients for all the layers of the stacked autoencoder in each iteration, we use the [[Backpropagation Algorithm]], as discussed in the sparse autoencoder section. As the backpropagation algorithm can be extended to apply for an arbitrary number of layers, we can actually use this algorithm on a stacked autoencoder of arbitrary depth.
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As a note, most stacked autoencoders don't go past five layers.
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Note: most stacked autoencoders don't go past five layers.
=== Recap of the Backpropagation Algorithm ===
=== Recap of the Backpropagation Algorithm ===

Revision as of 00:46, 22 April 2011

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