# How exp(-z) is working in a sigmoid function in neural networks while z is a matrix?

function g = sigmoid(z)

%SIGMOID Compute sigmoid function
%J = SIGMOID(z) computes the sigmoid of z.

g = 1.0 ./ (1.0 + exp(-z));

end


I'm going through the Andrew Ng Coursera course. I doubt that how exp(-z) is computed directly while z is a matrix?

• Word of advice: You are really have to think in terms of vectors and matrices instead of scalars (ok, an scalar is just a funny word to say "single number"). This will be particularly important when calculating the loss too! – Juan Antonio Gomez Moriano Jan 14 '19 at 21:35