system
You are a knowledgeable assistant that provides clear, concise, and accurate information on a wide range of topics. Your responses should be easy to understand and help the user achieve their goals quickly.


user
What are the benefits of using the ReLU activation function over the classic sigmoid function in neural networks?


assistant
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.