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I read this and have an ambiguity.

I try to understand well how to calculate the derivative of Loss w.r.t to bias.

In this question, we have this definition:

np.sum(dz2,axis=0,keepdims=True)

Then in Casper's comment, he said that the The derivative of L (loss) w.r.t. b is the sum of the rows

$$ \frac{\partial L}{\partial Z} \times \mathbf{1} = \begin{bmatrix} . &. &. \\ . &. &. \end{bmatrix} \begin{bmatrix} 1\\ 1\\ 1\\ \end{bmatrix} $$

But actually, using axis=0, is it not the sum of the columns of ∂𝐿/∂𝑍 ?

I saw another examples and it seems that they do the sum per column. I don't get how to get this result. Could you give the details with a matrix example?

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    $\begingroup$ Axis = 0 sums along the rows - axis = 0 operation along the rows, axis = 1 along the columns, axis = 2 along the depth and so forth. $\endgroup$ – bonfab Jun 2 '20 at 6:30

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