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About Neural Networks For Machine Learning: Lecture 14A Learning Layers of Features by Stacking Rbms
This document provides an overview of training deep neural networks by stacking restricted Boltzmann machines (RBMs). It discusses: - Training each layer of an RBM to learn features from the activations of the previous layer, then treating those activations as inputs to the next layer. - Each additional layer improves a variational lower bound on the log probability of generating the training data. - Combining trained RBMs into a deep belief network (DBN) by using the activations of one RBM as the inputs to train the next RBM.
Detailed Information
| Author: | ['Tyler Roberts'] |
|---|---|
| Publication Year: | 2016 |
| Pages: | 39 |
| Language: | English |
| Format: | |
| Price: | FREE |
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