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About 3048 Greedy Layer Wise Training of Deep Networks
This document summarizes research on training deep neural networks. It discusses how deep multi-layer neural networks can compactly represent complex functions through many levels of non-linearities. However, it was previously difficult to train deep networks through gradient descent from random initialization. The paper explores greedy layer-wise unsupervised pre-training of deep belief networks (DBNs), where each layer is trained one at a time using unsupervised learning before fine-tuning the entire network. Experiments show this pre-training strategy helps optimization by initializing weights near good local minima and creating high-level feature representations that improve generalization.
Detailed Information
| Author: | ['Parag Darade'] |
|---|---|
| Publication Year: | 2015 |
| Pages: | 8 |
| Language: | English |
| Format: | |
| Price: | FREE |
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