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In artificial neural networks, the activation function of a node is a function that calculates the output of the node based on its individual inputs and their weights. Nontrivial problems can be solved using only a few nodes if the activation function is nonlinear.
Art, Mathematical details & Comparison of activation functions
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| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| superposition can be preserved by creating the Taylor series of the argument computed by the perceptron itself | instance of | The quantum properties loaded within the circuit | 0.80 | text |
| with suitable quantum circuits computing the powers up to a wanted approximation degree | instance of | The quantum properties loaded within the circuit | 0.80 | text |
| Activation function | related to Comparison of activation functions | Aside | 0.60 | section |
| Activation function | related to Comparison of activation functions | These | 0.60 | section |
| Activation function | related to Comparison of activation functions | For | 0.60 | section |
| Activation function | related to Folding activation functions | Folding | 0.60 | section |
| Activation function | related to Folding activation functions | These | 0.60 | section |
| Activation function | related to Folding activation functions | In | 0.60 | section |
| Activation function | related to Further reading | Kunc | 0.60 | section |
| Activation function | related to Further reading | Vladimír | 0.60 | section |
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