In video ‘Neural Network Presentation’ of week 3, W^{[1]} is shown as a (4, 3) matrix and b as (4,1) vector. From my understanding, this means that each row is equal to the parameter vectors of the first layer, and that w^{[2]}_3 is the row vector of parameters of the fourth layer and third neuron.

Apparently (see screenshot), this is not the case. So can anyone help with the definitions of W and perhaps X as well? If I remember correctly, in one of the videos it’s mentioned that features are stored as columns (which implies that each example is stored as a row), but in practice it seems that each example is stored as a column instead.

Any help or guidance would be appreciated, thanks!