so i have looked through the videos a few times and still dont really understand how to code it out. i am at the conv_forward function.
my questions are
- what does the m in A_prev represent? is it the output of eat image in the training set?
- what does use the corners to define the (3D) slice of a_prev_pad mean? like what is a 3d slice
- what does convolvingthe (3D) slice with the correct filter W and bias b, to get back one output neuron? 1 neutron as in the first row of output from 1 image or all the images collectively ?
once again sorry if my questions are super amateur. also if anyone doesn’t mind discussing topics like this on discord do drop me a dm!!
@zheng_xiang1
I am going to ask some guiding questions.
- I think you are talking this
m
. It means the number of samples.
A_prev -- output activations of the previous layer,
numpy array of shape (m, n_H_prev, n_W_prev, n_C_prev)
-
How do you understand this hint given in the assignment?
-
Why do you take a slice out? (How are you going to do on that slide with a filter?)
Raymond
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It’s fine. Let’s look forward 
But how to code it is what the assignment is about. I can’t tell you how to code it. What about this video? The lecture gave a pretty intuitive example (edge detection) of how to apply a filter on a slice of data. Indeed, this is also what Exercise 2 does and you should have finished it, since your question is about Exercise 3.
Raymond
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