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About Dyn Am Learn
This document presents an analytical study of the nonlinear learning dynamics in deep linear neural networks. The study finds exact solutions to the differential equations that describe how the weights in these networks change over time during gradient descent learning. These solutions show that deep linear networks can exhibit nonlinear learning phenomena like long periods of little change followed by rapid improvement, and that unsupervised pretraining can provide faster learning than random initialization in some cases. The solutions also indicate that learning time may remain finite even as network depth increases indefinitely under certain conditions.
Detailed Information
| Author: | ['reader21'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 21 |
| Language: | English |
| Format: | |
| Price: | FREE |
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