In the text I read the following:
I’m confused on the dimensions of the bias vector. How can we add a
(m,1) vector to a
(1, p) vector? Is
w0 shaped correctly? Or should
w1 be shaped
(n, P) to account for
P classes, and the we broadcast
Note: I assume
w1 should be
(n, P) so that our matrix multiplication yields a row of unnormalized logits for each class prediction for each observation. Then does it make sense to add a per-class bias and broadcast that to the number of samples in our data?
I feel foolish for even asking but walking through the example I couldn’t reconcile...