Neural Networks Fundamentals Assessment

What distinguishes ReLU from sigmoid and tanh activation functions?

ReLU is differentiable everywhere unlike sigmoid and tanh ReLU does not saturate for positive values, avoiding vanishing gradients in that region ReLU outputs probabilities between 0 and 1 ReLU is a second-order polynomial function

Correct Answer:

ReLU does not saturate for positive values, avoiding vanishing gradients in that region

Access more questions from this quiz

Continue to Neural Networks Fundamentals Assessment for more practice questions and the full quiz experience.

Access More Questions