1.4 is simply the output of a sigmoid neuron

To put it all a little more explicitly, the output of a sigmoid neuron with inputs x1,x2,, weights w1,w2,, and bias b is 1+exp(jwjxjb)1 a=(wa+b) There's quite a bit going on in this equation, so let's unpack it piece by piece. a is the vector of activations of the second layer of neurons. To obtain a we multiply a by the weight matrix w, and add the vector b of biases. We then apply the function elementwise to every entry in the vector wa+b6. It's easy to verify that Equation 1.22 gives the same result as our earlier rule, Equation 1.4, for computing the output of a sigmoid neuron. Exercise - Write out Equation 1.22 in component form, and verify that it gives the same result as the rule 1.4 for computing the output of a sigmoid neuron