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About Why Does Unsupervised Pre-Training Help Deep Learning?
1. The document explores how unsupervised pre-training helps with training deep learning models through extensive experimentation. 2. It argues that unsupervised pre-training acts as a form of regularization that minimizes variance and biases the parameters towards configurations useful for unsupervised learning, placing the model in a region of parameter space that supports better generalization. 3. Even with large amounts of labeled data, unsupervised pre-training can still provide benefits because stochastic gradient descent is sensitive to initialization and early parameter changes, and pre-training sets the initialization in a basin of attraction that leads to better generalization.
Detailed Information
| Author: | ['AndersonCarlos'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 36 |
| Language: | English |
| Format: | |
| Price: | FREE |
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