ReLU Activation Function

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neural-networks Google Meta OpenAI

What is the primary advantage of using the ReLU activation function over the sigmoid activation function in deep neural networks?

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ReLU outputs values between 0 and 1, making it easier to interpret ReLU mitigates the vanishing gradient problem and is computationally cheaper to evaluate ReLU always produces non-zero gradients, ensuring all neurons learn ReLU is differentiable everywhere, unlike sigmoid

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ReLU Activation Function

Multiple Choice

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