Backpropagation calculus | Chapter 4, Deep learning

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This one is a bit more symbol-heavy, and that’s actually the point. The goal here is to represent in somewhat more formal terms the intuition for how backpropagation works in part 3 of the series, hopefully providing some connection between that video and other texts/code that you come across later.

For more on backpropagation:
http://neuralnetworksanddeeplearning.com/chap2.html
https://github.com/mnielsen/neural-networks-and-deep-learning
http://colah.github.io/posts/2015-08-Backprop/

Music by Vincent Rubinetti:
https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown

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Video timeline
0:00 – Introduction
0:38 – The Chain Rule in networks
3:56 – Computing relevant derivatives
4:45 – What do the derivatives mean?
5:39 – Sensitivity to weights/biases
6:42 – Layers with additional neurons
9:13 – Recap
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