The state of the art of non-linearity is to use rectified linear units (relu) instead of sigmoid function in deep neural network. What are the advantages? I know that training a network when relu is

How does that improve neural network? Why do we say that relu is an activation function? Isn't softmax activation function for neu

Relu Merisanu

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Relu Merisanu
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2020: Phase transitions in layered neural networks: ReLU vs. sigmoidal
2020: Phase transitions in layered neural networks: ReLU vs. sigmoidal
2020: Phase transitions in layered neural networks: ReLU vs. sigmoidal
2020: Phase transitions in layered neural networks: ReLU vs. sigmoidal
2020: Phase transitions in layered neural networks: ReLU vs. sigmoidal
2020: Phase transitions in layered neural networks: ReLU vs. sigmoidal
2020: Phase transitions in layered neural networks: ReLU vs. sigmoidal