DeepMind's AlphaGo Zero algorithm beat the best Go player in the world by training entirely by self-play. It played against itself repeatedly, getting better over time with no human gameplay input. AlphaGo Zero was a remarkable moment in AI history, a moment that will always be remembered. Move 37 in particular is worthy of many philosophical debates. You'll see what I mean and get a technical overview of its neural components (code + animations) in this video. Enjoy!
Code for this video:
https://github.com/Zeta36/chess-alpha-zero
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There are 2 errors in this video:
1. At the top of the residual network, it says value layer twice. One should say 'policy' layer.
2 The residual network is 40 layers, i say 20.
This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine_Learning_Journey
More Learning Resources:
https://deepmind.com/blog/alphago-zero-learning-scratch/
https://medium.com/applied-data-science/alphago-zero-explained-in-one-diagram-365f5abf67e0
https://hackernoon.com/the-3-tricks-that-made-alphago-zero-work-f3d47b6686ef
https://web.stanford.edu/~surag/posts/alphazero.html
http://tim.hibal.org/blog/alpha-zero-how-and-why-it-works/
http://www.jessicayung.com/alphago-zero-an-overview-of-the-algorithm/
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