Pros:

• Computationally efficient—allows the network to converge very quickly
• Non-linear—although it looks like a linear function, ReLU has a derivative function and allows for backpropagation.
• If you're not sure what activation function to use for the hidden layers, it's better to use ReLU by default.
• Jeffery Hinton: Allows neuron to express a strong opinion
• Gradient doesn't saturate (on the high end)
• Less sensitive to random initialization
• Runs great on low precision hardware

Cons:

• The Dying ReLU problem (Dead neuron): when inputs approach negative values, the gradient of the function becomes zero, the network cannot perform backpropagation and cannot learn. => Solution: Leaky ReLU
• Gradient discontinuous at origin: when inputs equal to 0, there is no derivative since it's in the intersection of a horizontal line and a linear line. So learning is not happening there. => Solution: GELU