DeepMind - AlphaCode
AlphaCode by DeepMind offers an interactive platform for solving programming challenges in C++ with community engagement and performance insights.
Deepmind
Explore how moves played by AlphaGo compare to those of professional and amateur players. This tool provides analysis of thousands of the most popular opening sequences from the recent history of Go, using data from 231,000 human games and 75 games that DeepMind's AlphaGo played against human players
AlphaGo 教学： 探索围棋创新思路 - Deepmind
主页; 关于. 关于 WGo.js player 2.0; 关于 DeepMind AlphaGo; 感谢. 6,000 种开局变化来自于 超过 230,000 盘人类对局: 来自 Go4Go 的 65,000 局职业比赛 (1980-2017) 来自 GoBase 的 36,000 局职业比赛 (1960-1990) 野狐围棋 的 130,000 盘野狐围棋9段棋手对局; 75 盘 AlphaGo 与人类棋手对 …
Ithaca | Restoring and attributing ancient texts using deep neural …
Restoration of a damaged inscription, recording a decree from 485/4 BCE concerning the Acropolis of Athens (IG I 3 4B, CC BY-SA 3.0, WikiMedia).. While Ithaca alone achieves 62% accuracy when restoring damaged texts, as soon as historians use Ithaca their performance leaps from 25% to 72%, confirming this synergistic research aid's impact.
AlphaGo Teach: 바둑에서 새롭고 창의적인 수들을 발견해보세요
알파고의 수들이 프로 및 아마 강자의 수들과 어떻게 다른지 알아보세요. 이 툴은 총 231,000판의 사람 간 대국과 딥마인드 알파고의 대국 75판을 분석해 선별한 가장 많이 두어진 포석 수 천가지의 연구 결과를 제공합니다
Highlighted research - deepmind.com
Our pioneering research includes Deep Learning, Reinforcement Learning, Theory & Foundations, Neuroscience, Unsupervised Learning & Generative Models, Control & Robotics, and Safety.
AlphaGo教學: 探索圍棋創新思路 - Deepmind
首頁; 關於. 關於WGo.js player 2.0; 關於DeepMind AlphaGo; 感謝. 6,000 種開局變化來自於 超過 230,000 盤人類對局: Go4Go 的 65,000 局職業比賽 (1980-2017) GoBase 的 36,000 局職業比賽 (1960-1990) 野狐圍棋 的 130,000 盤野狐圍棋9段棋手對局; 75 盤 AlphaGo 與人類棋手對局 (前 30 …
AlphaGo 学習ツール: 新たな囲碁の楽しみを - Deepmind
AlphaGo と棋士たちの手を比較してみましょう。このツールは、よくある序盤パターンを、人同士が打った 231,000 局と、DeepMind 開発の AlphaGo が人と対局した 75 局の棋譜データから分析することができます。