Which loss function should you use to train your machine learning model? The huber loss? Cross entropy loss? How about mean squared error? If all of those seem confusing, this video will help. I'm going to explain the origin of the loss function concept from information theory, then explain how several popular loss functions for both regression and classification work. Using a combination of mathematical notation, animations, and code, we'll see how and when to use certain loss functions for certain types of problems.
Code for this video:
https://github.com/llSourcell/loss_functions_explained
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More Learning Resources:
http://www.informit.com/articles/article.aspx?p=2447200&seqNum=2
https://medium.com/data-science-group-iitr/loss-functions-and-optimization-algorithms-demystified-bb92daff331c
http://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html
https://blog.algorithmia.com/introduction-to-loss-functions/
http://yeephycho.github.io/2017/09/16/Loss-Functions-In-Deep-Learning/
https://stackoverflow.com/questions/42877989/what-is-a-loss-function-in-simple-words
http://rohanvarma.me/Loss-Functions/
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