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About How To Choose An Activation Function For Deep Learning
The document discusses choosing activation functions for deep learning neural networks. It explains that activation functions transform weighted inputs into outputs for each node in a layer. The choice of activation function affects how well a network can learn. For hidden layers, rectified linear activation (ReLU) is most commonly used due to its effectiveness. For output layers, the activation depends on the prediction task, such as sigmoid for classification and linear for regression. The document provides details on ReLU, sigmoid, and hyperbolic tangent activation functions.
Detailed Information
| Author: | ['Abdul Khaliq'] |
|---|---|
| Publication Year: | 2022 |
| Pages: | 15 |
| Language: | English |
| Format: | |
| Price: | FREE |
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