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About Activation Functions and Their Characteristics in Deep Neural Networks
The document discusses activation functions and their characteristics in deep neural networks. It begins by introducing activation functions and their role in artificial neural networks as analogs to biological neurons. It then discusses several commonly used activation functions - sigmoid, hyperbolic tangent, ReLU, and their variants. It analyzes the characteristics of these functions, including their impacts on training deep neural networks and addressing issues like vanishing gradients. Experimental results on MNIST are also discussed to compare performance of different activation functions.
Detailed Information
| Author: | ['Satyam'] |
|---|---|
| Publication Year: | 2023 |
| Pages: | 6 |
| Language: | English |
| Format: | |
| Price: | FREE |
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