Backpropagation algorithm is based on complex mathematical calculations. That’s why, it is hard to understand and that is the reason why people have an edge on neural networks. Adapting the concept into the real world makes even Homer Simpson, who has a low IQ of 55, easier to figure out. In this post, we’ll mention how to explain backpropagation to beginners.
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What if an approved loan application would be outstanding loan (or dead loan)? The bank loses money. So, how can this financial institution derive lesson from this mistake?
Loan application is a process. In other words, an application is required to be examined by multiple authorized employees respectively. For instance, a customer makes an application to bank branch agent, then agent delivers the application to branch supervisor or branch manager. After then, head office employees examine the application when branch manager approved. To sum up, a loan application follows a path and comes to hands of in charged of employees. Should these employees responsible for the lose? The answer is yes based on backpropagation.
Backpropagation algorithm proposes to reflect the lost money amount on the same path, but backwardly. That’s why, it is named as back-propagation. Fine head office employees first, then punish branch manager, supervisor, and agent respectively. What’s more, how much the total lose amount should be reflected to a branch agent? Total lose amount should be divided between in charged of employees based on their contributions on total lose. (Actually, that is the derivative of total lose amount with respect to the employee. E.g. ∂TotalLoseAmount / ∂BranchAgent). In this way, these employees would be more careful in the next time. That is the principle of backpropagation algorithm. Thus, examination process would progress in time.
Dropping the subject with a metaphor
As the phrase goes, backpropagation advices slapping ones who are on the tracked path backwardly and in the ratio of their contribution on total error. I would like to thank Dr. Alper Ozpinar for this metaphor.
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