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I want to implement back-propagation neural network algorithm in raw code, not using any library as my interest to understand in depth operations/customization of this algorithm.

In this regard, I need to follow step-by-step simulation of back-propagation algorithm. More precisely, what code I should write in R language to each of steps in back-propagation neural network. Can anyone show me the close explanation of steps in this algorithm.

I don't need any code. Just show me what operations is done in each steps.

FYI: for reviewers, this is not my assignment. I'm doing my assignments using neuralnet library. Please leave a comment before any report.

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  • $\begingroup$ Would a step by step derivation of a vanilla neural network help you? $\endgroup$
    – Armen Aghajanyan
    Commented Dec 15, 2015 at 20:01
  • $\begingroup$ @ArmenAghajanyan Can you provide any example? $\endgroup$
    – mmr
    Commented Dec 16, 2015 at 3:58

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If you just want to see how the internals work, the best resource for a beginner, that I have found, is this series of videos by Welch Labs. It goes through each step of a basic feed-forward backpropogation model including the calculus steps so you understand what is happening during each step. It is all done in Python but you can definitely convert it to R if that is your goal. That would be a great learning exercise.

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