What are the benefits of using the ReLU activation function over the classic sigmoid function in neural networks?
1. Gradient vanishing problem: The max value of the derivative of sigmoid function is 0.25, which can result in the total gradients becoming very small during back propagation. In contrast ReLU has a constant gradient 1 for positive inputs. 
2. Compute efficiency: ReLU can be computed using max(0, x), more efficient than exponential operations in sigmoid function. 
3. Convergence speed: it's also reported that using ReLU as activation function in neural networks converges faster than using sigmoid function.
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Answer the following query.
What are the benefits of using the ReLU activation function over the classic sigmoid function in neural networks?

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1. Gradient vanishing problem: The max value of the derivative of sigmoid function is 0.25, which can result in the total gradients becoming very small during back propagation. In contrast ReLU has a constant gradient 1 for positive inputs. 
2. Compute efficiency: ReLU can be computed using max(0, x), more efficient than exponential operations in sigmoid function. 
3. Convergence speed: it's also reported that using ReLU as activation function in neural networks converges faster than using sigmoid function.
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