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7 thoughts on “Entropy (for data science) Clearly Explained”
Send me the note in pdf
Send me the note on entropy in pdf.
You are one of the geniuses in teaching I have ever come across.
Thank you very much Josh!
I am dissatisfied with your explanation for surprise being log(1/p) on the basis that it is 0 when p is 1 and infinity when p is 0. There are a zillion functions of p with those limits. Why is log(1/p) preferred over all of the others? Is it just a convention chosen to match the physics definition and because of the convenient mathematical properties of the log? Or is there something more to it?
If you want a more mathematically grounded explanation, I would highly recommend the original manuscript by Shannon: https://people.math.harvard.edu/~ctm/home/text/others/shannon/entropy/entropy.pdf
I believe it might have something to do with simple calculations to compute gradients when softmax is paired with the cross-entropy loss.