Computation graph, where does the N+P instead of NxP comes from

Course Q&A Machine Learning Specialization Advanced Learning Algorithms
1

it is possible to have more details about why the computational complexity using a computation graph moves from NxP to N+P? I can’t easily grasp it from the lectures.

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Can you point to the video lecture you are referring to ? probably a link to the lecture video

Regards
DP

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Hi @buzzelliart,

I happen to remember this reply of mine so I am sharing it here. Next time, please do as @Deepti_Prasad asks, because we need your help to see what you saw.

Cheers,
Raymond

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Thank you Raymond again

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Hi @buzzelliart,

As Andrew mentioned in the course, if we want to compute derivatives left to right (Forward prop),
Then we should compute it for each parameters one at a time. So it is: NxP.
But then, back prop method let us compute all parameter’s derivatives in just one run. So it is: N+P.
Hope it is helpful.

Cheers,
Amir

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thank you Raymond, this makes things way more clear.

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thank you to everyone that helped.
For completeness of the question, it is referred to this lecture:

Advanced Learning Algorithms → Week 2 → Computation graph (Optional)

Computation graph (Optional) | Coursera

timestamp ~ 17:50

Thank you for the prompt help!

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