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caoliu2019最新地址一二
2007年第三季度广告服务收入达8,550万元人民币(1,140万美元),上一季度和去年同期分别为6,530万元人民币(870万美元)和8,340万人民币(1,110万美元)。 【截】【止】【2】【0】【0】【2】【年】【9】【月】【3】【0】【日】【,】【网】【易】【的】【日】【平】【均】【页】【面】【浏】【览】【量】【超】【过】【了】【亿】【人】【次】【。】【网】【易】【公】【司】【的】【网】【站】【已】【有】【超】【过】【8】【,】【0】【7】【0】【万】【名】【登】【记】【用】【户】【,】【 】【5】【5】【,】【4】【7】【6】【位】【聊】【天】【室】【的】【同】【时】【使】【用】【者】【。】 【每】【当】【有】【一】【个】【新】【事】【物】【出】【现】【时】【,】【 】【总】【有】【人】【想】【当】【然】【地】【以】【为】【对】【身】【体】【不】【好】【,】【 】【这】【就】【是】【像】【”】【照】【相】【机】【能】【摄】【魂】【”】【,】【 】【“】【手】【机】【致】【脑】【癌】【”】【,】【 】【“】【W】【I】【F】【I】【会】【流】【产】【”】【一】【样】【。】【 】【眼】【睛】【虽】【然】【离】【屏】【幕】【是】【很】【近】【,】【 】【但】【是】【并】【不】【是】【聚】【焦】【在】【近】【距】【离】【上】【,】【 】【因】【为】【有】【凸】【透】【镜】【的】【存】【在】【,】【 】【你】【看】【的】【是】【几】【米】【甚】【至】【上】【百】【米】【远】【的】【东】【西】【。】【 】【比】【起】【躺】【床】【上】【看】【手】【机】【来】【说】【,】【 】【眼】【睛】【受】【的】【伤】【害】【小】【的】【多】【。】【 】【如】【果】【非】【要】【挑】【一】【个】【可】【能】【对】【眼】【镜】【有】【负】【面】【作】【用】【的】【因】【素】【的】【话】【,】【 】【我】【想】【低】【刷】【新】【率】【才】【是】【值】【得】【关】【注】【的】【。】【 】【所】【以】【,】【 】【请】【不】【要】【长】【时】【间】【体】【验】【基】【于】【手】【机】【的】【V】【R】【产】【品】【,】【 】【因】【为】【它】【们】【的】【刷】【新】【率】【多】【数】【连】【6】【0】【 】【F】【P】【S】【都】【不】【到】【,】【 】【会】【导】【致】【视】【觉】【疲】【劳】【并】【引】【起】【晕】【动】【症】【。】 其中Policy Network用来在Selection和Expansion阶段,衡量为每一个子节点打分,找出最有希望、最最需要预先展开的那个子节点。Policy Network网络的训练,是通过观察其他人类之间对弈的棋局来学习的,主要学习的目标是:“给定一个棋局,我接下来的一步应该怎么走”?(这是一个静态的过程,不用继续深入搜索更深层的子节点)为此,AlphaGo先读取KGS(一个网络围棋对战平台)上面近16万局共3000多万步的人类走法,通过Supervised Learning的方法,学习出来一个简单的SL Policy Network(同时还顺便训练出来Simulation阶段用来一路算到决胜局使用的Rollout Policy)。然后基于这个在人类棋局上学习出来的SL Policy Network, 使用强化学习(Reinforcement Learning)的方法通过自己跟自己对弈,来进一步优化Policy Network。这么做的原因,一个可能的原因是通过人类棋局学出来的SL Policy Network,受到了人类自身能力的局限性的影响(KGS棋局中包含了很多非专业棋手,实力层次不齐),学不出特别好的策略来。那不如在此基础上,自己跟自己打,在此过程中不断学习不断优化自己的策略。这就体现了计算机的优势,只要不断电,计算机可以不分昼夜不断自己跟自己下棋来磨练棋艺。RL Policy Network初始参数就是SL Policy Network的参数,但青出于蓝而胜于蓝,实验指出RL跟SL策略对弈,RL胜率超过80%。RL Policy Network也是最终应用在实际对战过程中MCTS Selection阶段的策略。 但是我们还是认为人类是特殊的,比如有情感,比如有灵感,比如有抽象能力,能通过有限推广到无限(比如数学里的极限),……。这些能力不管是被上帝赋予的还是人类“生下来就有的”(那第一个人怎么获得这么bug的技能的呢?),反正我们就认为其它“东西”是不可能拥有的。(虽然我个人认为人类没有任何特殊的能力,就不展开讨论了)Read More
Published on Oct-5-2019 01:15:17 PM

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Running days:

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