I went through all of the links which I’ve given below and find them to be a comprehensive guide to understanding backpropagation. Please consider going through below mentioned awesome tutorials and articles for getting a solid grip on backprop as it is the backbone of neural networks.

1. http://cs231n.stanford.edu/handouts/linear-backprop.pdf

2. http://cs231n.github.io/optimization-2/

3. http://neuralnetworksanddeeplearning.com/chap2.html

4. https://github.com/dennybritz/nn-from-scratch/blob/master/nn-from-scratch.ipynb

5. http://www.wildml.com/2015/10/recurrent-neural-networks-tutorial-part-3-backpropagation-through-time-and-vanishing-gradients/

6. https://www.analyticsvidhya.com/blog/2017/05/neural-network-from-scratch-in-python-and-r/