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About Large Scale Deep Learning
Here are the gradients computed analytically: ∂Y/∂H1 = H1 > 0 ? 1 : 0 (derivative of ReLU) ∂H1/∂H0 = 1 (identity function) ∂H0/∂W = X (matrix multiplication) ∂H0/∂X = W (matrix multiplication) So the overall gradients are: ∂Y/∂W = ∂Y/∂H1 * ∂H1/∂H0 * ∂H0/∂W = X * (H1 > 0 ? 1 : 0) ∂Y/∂
Detailed Information
| Author: | ['pavancreative81'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 170 |
| Language: | English |
| Format: | |
| Price: | FREE |
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