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I am following this guide on calculating the partial derivatives of weights and biases:

https://www.datahubbs.com/deep-learning-101-the-theory/

Here it is using 1 hidden layer. How can I calculate the backpropagation if I add another hidden layer? Assuming it is using the sigmoid activation function same as the guide. Thanks!

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Sadly it's extremely difficult to type out mathematical equations here, so I've linked some backpropagation derivation notes that I found useful.

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  • $\begingroup$ Thank you! The link was helpful too. $\endgroup$
    – Dametime
    Jan 6, 2020 at 21:54

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