... 去拿水桶 Get the bucket 去拿你东西 Go Get Your Things 去银行拿回单 Single back to the bank ; Go to the bank receipt ...
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去,拿上你的东西,去!
One of the things we'll see as we go on, is you can get all your statistics right, and still get the wrong answer because of a consistent bug.
在我们运行过程中,我们看到你可以让所有的统计值是正确的,但依然得到了错误的答案,这可能是有什么连续性错误。
But where things get interesting is that with this model, you can actually gauge your bandwidth as you go. Then you can grow your app beyond the free quotas.
然而,有趣的是,使用这种模式时,实际上可以不断估计带宽,逐渐扩展应用程序,直至超出免费范围。
Successive approximation, Newton-Raphson was one nice example, but there's a whole class of things that get closer and closer, reducing your errors as you go along.
逐渐逼近,牛顿迭代是一个很好的例子,随着你不断的时行下去,你会不断的离结果越来越近,逐渐地减少误差。
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