2017-11-03

[public] 1.57M views, 59.7K likes, 254 dislikes audio only

3Blue1Brown

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Special thanks to these supporters: http://3b1b.co/nn3-thanks

Written/interactive form of this series: https://www.3blue1brown.com/topics/neural-networks

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

Thanks to these viewers for their contributions to translations

Hebrew: Omer Tuchfeld

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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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