So these are the steps to compute the forward propagation of our neural network.
Over here we are using the function g to denote an activation function.
Back propagation involves computing the derivative with respect to our parameters so that we can use
that derivatives to update our parameters.
Um that's a summary of the um the equations we've seen so far.
And over here we are using the vector ization method of course out.
I would advise you post a video over here and make sure you recognize the equations.
If I have any questions just let me know and actually see you in the next lesson.