What is the translation of " YOUR ALGORITHM " in Chinese?

• 你的算法

Examples of using Your algorithm in English and their translations into Chinese

Step 3- Now move to your algorithm.
步骤3：现在切换到您的Chromebook。

Note: Your algorithm should have a linear runtime complexity.
备注：你的算法应该是一个线性时间复杂度。

Using a loop invariant, prove that your algorithm is correct.
使用一个循环不变式来证明你的算法是正确的。

Your algorithm should run in O(n) time and uses constant space.
你的算法应该运行在O(n)O(n)时间复杂度，并且使用常量空间。

Second, you can use human performance as the optimal error rate that you want to reach with your algorithm.
其次，你可以使用人工性能作为你希望通过算法达到的最佳错误率。

Does your algorithm expire after six months, and is it interpretable?”.
您的算法是否在六个月后过期，它是否可以解释？”.

Also, once you start looking through these examples, you will probably find new ideas on how to improve your algorithm.
此外，一旦你开始查看这些样本，你可能会发现如何改进算法的新思路。

Train your algorithm with high quality data to increase user satisfaction.
利用高质量数据训练您的算法、以提高用户满意度.

It will draw a comprehensive picture and cover all topics necessary for further advancement of your algorithms knowledge.
它将绘制一幅全景图，并涵盖所有有必要的课题，进一步提高读者的算法知识。

Do your algorithms recognize when the regime has changed or do you need humans for that?
你们的算法能否意识到机制已经改变，还是需要人类的帮助？?

If you test the input against even numbers, your algorithm will be slower by a factor of 2(you test double the numbers).
如果你测试输入偶数，你的算法将慢2倍（你测试双倍数字）。

Your algorithm splits each number into four smaller numbers, then sends each to a sub-algorithm.
你的算法将每个大数字分成四个较小的数字，然后将每个小数字发送给子算法。

If Python becomes your bottleneck(you have optimized your algorithm), then you can turn to popular Cython or C.
如果Python成为你的瓶颈（你已经优化过你的算法），那么可以转向热门的Cython或者C。

Realizing that your algorithm is saving lives and helping society is very motivating.”.
意识到你的算法正在拯救生命和帮助社会，这是非常鼓舞人心的。

Machine learning systems can't explain their thinking, and that means your algorithm could be performing well for the wrong reasons.
机器学习系统无法解释它们的想法，这意味着你的算法可能因错误的原因而表现良好。

Your algorithm splits each number into four smaller numbers, then sends each to a sub-algorithm.
你的算法将每个数字分成四个较小的数字，然后将每个数字发送到一个子算法。

You might, for example, dismiss the value component, making your algorithm less sensitive to the light conditions of the input image.
例如，您可能会忽略最后一个组件，使您的算法对输入图像的光线条件不那么敏感。

This prevents your algorithm from learning what really separates cats from dogs and totally confuses it.
这可以防止你的算法学习真正将猫与狗分开的东西然后完全混淆它们。

Your computer vision system could not figure it out, but I'm happy to tell you and do my bit make your algorithms better.
你的计算机视觉系统无法弄清楚，但我很乐意告诉你，并尽我所能，使你的算法变得更好。

If you are going to share your algorithms with a partner understand that they are seeing your data.
如果你要与合作伙伴分享你的算法，那么他们就会看到你的数据。

Furthermore, you learned why the learning rate is it's most important hyperparameter and how you can check if your algorithm learns properly.
此外，你了解了为什么学习率是最重要的超参数，以及如何检查算法是否能够使你正确训练你的模型。

Once you have chosen and run your algorithm, there is one extremely important step left: visualizing and communicating the results.
一旦你选择并运行了你的算法，还有一个非常重要的步骤：可视化和交流结果。

Working with a good data set will help you to avoid or notice errors in your algorithm and improve the results of your application.
使用好的数据集可以帮助你规避或发现算法中的错误，改善程序的结果。

Once your algorithms are trained, they're not always used- your users will only call them when they need them.
当你的算法被训练好后，他们并不总是会被立刻使用，你的用户只会在他们需要的时候调用这些算法。

Imagine that you programmatically buy stocks based on your algorithm, and you're so profitable that you get a lot of money from investors.
想象一下，您根据自己的算法以编程方式购买了股票，并且您是如此有利可图，以至于您从投资者那里获得了很多钱。

Your algorithms are also running in a nine-year-old Chinese sedan that happens to have a first generation of your system.
你的算法也运行在一辆9年历史的中国轿车上，而这款车恰好是你的第一代系统。

High-quality, human-annotated data can be used to train your algorithm to deliver personalized results to your users based on their preferences and behavior.
可以使用高质量的人工标注数据训练您的算法、让您的算法能够根据用户偏好和行为提供个性化结果。

Making your algorithms faster(or changing to faster ones) can yield much larger benefits than trying to sprinkle micro-optimization tricks all over your code.
使您的算法更快（或更改为更快的算法）可以产生比尝试在代码中使用微优化技巧更大的好处。

Just because your algorithm can provide the correct output for this test case does not mean it can provide the correct output for every case.
如果你的算法仅仅可以为这个简单测试用例提供正确的输出，这并不意味着它可以为每个用例提供正确的输出。

The best exercise is to implement your own ML algorithm.
最好的练习是实现你自己的ML算法。