Gradient Descent for Neural Networks - Shallow Neural Networks | Coursera

[saifkhanengr]

@Suhail_Akhtar

How come did we calculated the shape of W and b for two layers?

I already answered this question in my first response. Also a thread Paul shared with you answered this:

@paulinpaloalto

So you end up with W^{[1]} having the dimensions n^{[1]} \times n_x, where n^{[1]} is the number of output neurons in layer 1 [and n_x is the number of input features (elements in each input x vector)]