I'm just an enthusiast. With that said, I think the author's first image image really captures the core of forward propagation and back propagation. For people

I'm just an enthusiast. With that said, I think the author's first image image really captures the core of forward propagation and back propagation. For people who want to get more of a feel on autodiffing, I find these 2 sources amazing [1, 2]. They don't use the word autodiffing, but looking at it, I can see it's the same thing. I once made an attempt at animating feed forward propagation [3]. If I'd animate back propagation, I'd probably make an animation on autodiffing as well as that is the most intuitive way I know on how to explain back propagation right now. It's still on my big to do list ;-) [1] http://karpathy.github.io/neuralnets/ http://karpathy.github.io/neuralnets/ [2] 3Blue1Brown does in episode 4 of his Deep Learning series: https://www.youtube.com/watch?v=tIeHLnjs5U8 https://www.youtube.com/watch?v=tIeHLnjs5U8 [3] https://melvinroest.github.io/Interactive-Neural-Networks/ https://melvinroest.github.io/Interactive-Neural-Networks/