Fine-tuning Stacked AEs
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=== Introduction === Fine tuning is a strategy that is commonly used in deep learning. As such, it can also be used to greatly improve the performance of a stacked autoencoder. From a high level perspective, fine tuning treats all layers of a stacked autoencoder as a single model, so that in one iteration, we are improving upon all the weights in the stacked autoencoder. === Strategy === Conceptually, fine tuning is quite simple. In order to view all layers of a stacked autoencoder as a single model, the gradients at each step are computed using the [[Backpropagation Algorithm]], as discussed in the sparse autoencoder section.
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