Pros:

• Smooth gradient, preventing “jumps” in output values.
• Output values bound between 0 and 1, normalizing the output of each neuron.
• Clear predictions—For X above 2 or below -2, tends to bring the Y value (the prediction) to the edge of the curve, very close to 1 or 0. This enables clear predictions.

Cons:

• Vanishing gradient—for very high or very low values of X, there is almost no change to the prediction, causing a vanishing gradient problem. This can result in the network refusing to learn further, or being too slow to reach an accurate prediction.
• Outputs not zero centered.
• Computationally expensive