Both of sigmoid function or perceptron decision function (step function) are differentiable.1 pointTrueFalse

Answer

False.

The sigmoid function is a differentiable function, which means it has a defined derivative at all points in its domain. This is crucial for optimization algorithms like gradient descent, where the slope of the function is used to determine the direction and magnitude of updates to the model's parameters.

On the other hand, the perceptron decision function, also known as the step function or Heaviside function, is not differentiable at the point where it changes from 0 to 1. This discontinuity means that the derivative is undefined at that specific point, and therefore, the perceptron decision function is not differentiable in the conventional sense.

In summary, while the sigmoid function is differentiable everywhere, the perceptron decision function is only piecewise constant, thus not differentiable due to the step discontinuity.