may i ask how to present đť‘§=đť‘¤đť‘‡đť‘Ą+đť‘Ź from array np.array([0,2]) ?

The mathematical expression for the â€ślinear activationâ€ť is:

z = w^T \cdot x + b

In that expression, w is a column vector of dimensions n_x x 1 and x is also a column vector of dimension n_x x 1, where n_x is the number of â€śfeaturesâ€ť or elements in each input. b is a scalar. The operation between w and x is the â€śdot productâ€ť which will end up giving us this sum:

\displaystyle \sum_{i = 1}^{n_x} w_i * x_i

The sum of the products of the elements of the two vectors. Then we add b which is a scalar to get the final answer for z.

The way you express that dot product operation in numpy is to use the function *np.dot*. That has been covered in the first exercise, right? Of course you have to transpose w first in order for the dot product to work: 1 x n_x dotted with n_x x 1 gives you a 1 x 1 or scalar output. The way you express transpose in numpy is just to write w.T or you can call the *np.transpose* function.

Also note that all this computation to produce z does * not* happen inside the

*sigmoid*function. You do that in

*propagate*and then you call

*sigmoid*with the result, right?

Also note that youâ€™ve posted in General Discussion again. I moved it for you again. Please use the DLS Course 1 category for asking questions about DLS Course 1.