10.17: Neural Networks: Backpropagation Part 4 - The Nature of Code

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In this video, I implement the formulas for "gradient descent" and adjust the weights in the train() function of my "toy" JavaScript neural network library.

Next video: https://youtu.be/tlqinMNM4xs

This video is part of Chapter 10 of The Nature of Code (http://natureofcode.com/book/chapter-10-neural-networks/)

This video is also part of session 4 of my Spring 2017 ITP "Intelligence and Learning" course (https://github.com/shiffman/NOC-S17-2-Intelligence-Learning/tree/master/week4-neural-networks)

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The Coding Train on Amazon: https://www.amazon.com/shop/thecodingtrain
Deeplearn.js: https://deeplearnjs.org/


Videos mentioned in this video:
My Neural Networks series: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6aCibgK1PTWWu9by6XFdCfh
3Blue1Brown Neural Networks playlist: https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi
My Video on Gradient Descent: https://youtu.be/jc2IthslyzM


Source Code for the all Video Lessons: https://github.com/CodingTrain/Rainbow-Code

p5.js: https://p5js.org/
Processing: https://processing.org


The Nature of Code playlist: https://www.youtube.com/user/shiffman/playlists?view_as=subscriber&shelf_id=6&view=50&sort=dd
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