If I test right up until the last thing I say, I don't think that gives you enough dwell time.
如果我正好测试我刚刚讲过的东西,我并不认为我会给你们足够的思考时间。
You're going to see the arguments about space if you take some of the courses that follow on, and again, some nice courses about that. For this course, we're not going to worry about space that much. What we're really going to focus on is time.
在以后的其他课程上你们,会学到一些关于空间的参数,一些讲这个的,很不错的课程,但是在这门课上,我们并不太关心空间问题,我们真正关心的是时间问题。
My watch was dissembled for a period of time, perhaps we should say my watch didn't exist during that period of time.
我的手表在修理的时候消失了,我们或许该说我的手表,在修理的那段时间,并不存在。
In our business, you got to be inventing new things because software doesn't wear out, it doesn't break, or at least if it breaks it was broken when you finished it, it doesn't break over time the way physical goods do.
在我们行业,你需要不断发明新事物,因为软件并不会磨损,不会坏,或者至少如果坏了,也是在你运行完之后,它并不像物理上的东西会随着时间磨损。
If there's no work to be done, 0 the running time is gonna be zero.
如果并不需要做什么,那么其运行时间肯定为。
Hartman's image of the simile isn't temporal the way Fish's was.
哈特曼关于这些比喻的描述并不是时间性的。
The last hundred years, in the U.S., don't really have that much predictive power.
在接下来的上百年时间里,在美国,我们并不具备那么强大的预测能力。
Yes-- for a while, but not for long.
是的,更快乐一段时间并不长久。
And so it didn't take long, once this vaccine were--was available, for people to want to get organized and think about ways of delivering this vaccine to all the regions of the world where people were potentially infected.
这并不会花费很多时间,一旦疫苗生产完毕,一些人就会组织起来,并想办法把将这些疫苗送往,世界上一些地方,那些地方的人很有可能感染疾病
Again, I'm not able to give this as much of a discussion as I would like because I don't want to spend too much time on this.
我要重申的是,我并不打算,进一步讨论这个问题,因为我并不打算在这上面花太多的时间。
Well, we could spend more time, but I'm not going to.
我们可以在这个问题上花更多的时间,但是我并不打算这么做
N log N is not nearly as good as log N. As a sanity check, what algorithm have we seen that runs in log N time?
而N,log,N和log,N并不一样,我们之前探讨过的哪个算法其时间复杂度是log,N呢?
The algorithm doesn't know that it's going to take this long to compute, it's just busy crunching away, trying to see if it can make it happen.
这很可能会发生,但是你知道的,算法本身并不知道,计算这个问题需要多长的时间,它就一直忙碌的算啊算。
For a couple of reasons. In some ways, this would be nicer, do expected cases, it's going to tell you on average how much you expect to take, but it tends to be hard to compute, because to compute that, you have to know a distribution on input.
关注最快的情况,在某种意义上来说,因为一些原因这样想挺不错的,当我们处理一个给定的问题,计算平均时间的时候,是很难计算的,因为你并不知道输入的分布情况,这些输入会是怎么样的呢?
Dictionaries are implemented using a magic technique called hashing, which we'll look at a little bit later in the term, which allows us to retrieve keys in constant time.
散列法的内容,此方法可以让我们在线性,时间内检索到键,因此字典的大小并不重要了。
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