How to calculate backpropagation derivatives for 2 hidden layers?

Question

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!

Answer 1

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