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Machine Learning for Computer Vision

AlphaGo •Policy CNN –Configuration -> choice of professional players –Trained with 30K+ professional games •Simulate till end to get binary labels •Value CNN –Configuration -> win/loss –Trained with 30M+ simulated games •Reinforcement learning, Monte-Carlo tree search •1202 CPUs + 176 GPUs •Beating 18 times world champion

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Mastering the game of Go with deep neural networks and ...

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our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a …

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卷积神经网络研究综述 - ict.ac.cn

cjc.ict.ac.cn

AlphaGo 主要采用价值网络（value networks）来评估棋盘的位置，用策略网络（policy networks）来选择下棋步法，这两种网络都是深层神经网络模型，AlphaGo 所取得的成果是深度学习带来的人工智能的又一次突破，这也说明了深度学习具有强大的潜力。

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Lecture 14: Reinforcement Learning

cs231n.stanford.edu

Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 14 - 1 May 23, 2017 Lecture 14: Reinforcement Learning

Learning, Reinforcement, Reinforcement learning

Mastering Chess and Shogi by Self-Play with a General ...

arxiv.org

AlphaGo Zero estimates and optimises the probability of winning, assuming binary win/loss outcomes. AlphaZero instead estimates and optimises the expected outcome, taking account of draws or potentially other outcomes. The rules of Go are invariant to rotation and reﬂection. This fact was exploited in AlphaGo and AlphaGo Zero in two ways.

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Mastering the Game of Go without Human Knowledge

discovery.ucl.ac.uk

AlphaGo was the ﬁrst program to achieve superhuman performance in Go. The published version 12, which we refer to as AlphaGo Fan, defeated the European champion Fan Hui in October 2015. AlphaGo Fan utilised two deep neural networks: a policy network that outputs move prob-abilities, and a value network that outputs a position evaluation.

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In Datacenter Performance Analysis of a Tensor Processing Unit

www.cs.virginia.edu

of GNM Translate [59]; one CNN is Inception; and the other CNN is DeepMind AlphaGo [53, 27]. 2. In-Datacenter Performance Analysis of a Tensor Processing Unit ISCA ’17, June 24-28, 2017, Toronto, ON, Canada the upper-right corner, the Matrix Multiply Unit is the heart of the

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