# Derivation: Derivatives for Common Neural Network Activation Functions

The material in this post has been migraged with python implementations to my github pages website.

Posted on September 8, 2014, in Classification, Derivations, Machine Learning, Neural Networks, Regression and tagged Backpropagation, backpropagation algorithm, Logistic Sigmoid, Neural Networks, Quotient Rule, Tanh Function. Bookmark the permalink. 9 Comments.

It was very helpful! Thanks!

What about softmax function?

Related: https://brenocon.com/blog/2013/10/tanh-is-a-rescaled-logistic-sigmoid-function/

Thank you for this great explanation!

I really very good what book do you follow ian livingworth??

Thanks for great explanation!

Video tutorial on Activation Functions:

https://quickkt.com/tutorials/artificial-intelligence/deep-learning/activation-function/

thanks a lot.

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