EPISODE · Aug 14, 2018 · 12 MIN
DeepMind AlphaGo Zero Explained
from Siraj Raval
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 Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology 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/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Sign up for the next course at The School of AI: https://www.theschool.ai And please support me on Patreon: https://www.patreon.com/user?u=3191693 #AlphaGoZero #Deepmind #SirajRaval
What this episode covers
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 Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology 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/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Sign up for the next course at The School of AI: https://www.theschool.ai And please support me on Patreon: https://www.patreon.com/user?u=3191693 #AlphaGoZero #Deepmind #SirajRaval
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DeepMind AlphaGo Zero Explained
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