YOUR MODEL - 翻译成中文

• 你的模型
• 你的模式
• 你的榜样
• 您的型号

在 英语 中使用 Your model 的示例及其翻译为 中文

You like photography, and you always made me your model.
你爱摄影，我做你的模特。

Your children will follow your model.
你们的孩子会效法你们的榜样。

This means you can use the normalized data to train your model.
这意味着您可以使用规范化的数据来训练您的模型。

Plan for how often your model will need to be retrained with updated data(e.g. perhaps you will retrain nightly or weekly).
计划你的模型需多久一次使用更新的数据进行再训练（如，你可能会每晚或每周进行再训练）.

We find the market and your model extremely interesting, so were keen to dig in deeper.
我们觉得市场和你的模式非常有趣，因此希望能在深入挖掘一下。

But you can't use that to prove your model is always as accurate as its cross-validation score, Ortiz explains.
但是你不能用它来证明你的模型总是和它的交叉验证分数一样准确，奥尔蒂斯解释道。

So as your model is close to reality, and it converges with feelings, you often don't know it's there.
所以随着你的模型越来越接近现实它将同感觉合二为一你将感觉不到它的存在.

If your model is not repairable, we will offer watches of the same value and style.
如果您的型号无法维修，我们将提供同等价值和款式相似的手表。

If you're a mathematician, researcher, or otherwise inclined to understand what your model is really doing, consider choosing PyTorch.
如果你是一名科研工作者，倾向于理解你的模型真正在做什么，那么就考虑选择PyTorch。

Getting your model just right will become much more complicated initially if the walls of the model are approaching the limit of 1mm.
恰到好处地让你的模型将变得更为复杂，最初的模式如果墙壁接近极限为1mm。

And of course if Microsoft is your model, you shouldn't be looking for companies that hope to win by writing great software.
当然M$就是你的模型，你不应寻找希望通过写好软件来获胜的公司。

If not, your model will degrade over time and won't perform as well, leaving your business to degrade too.
如果不是，你的模型会随着时间的推移而退化，并且不会表现得很好，从而导致你的业务也会退化。

As your model is in production, its important to update your model periodically, depending on how often you receive new data.
由于你的模型还在生产中，所以定期更新你的模型是很重要的，这取决于你接收新数据的频率。

And if your model is subscription, you must know your churn and lifetime value.
如果你的模型是订阅的，你必须知道你的客户流失和终身价值。

Just do not forget that if your model is going to a business meeting, curls and colorful strands are completely irrelevant.
只是不要忘了，如果你的模型是一个商务会议，卷发和丰富多彩的线是完全不相干的。

As you continue to make your model more complex, you end up over-fitting your model and your model will start suffering from high variance.
随着你继续让你的模型更复杂，你最终会过度拟合你的模型，你的模型将开始遭受高方差。

And in statistics all intervals are created equal, you don't pick only one for analysis, your model has to explain all intervals.
在统计学中所有的区间都是相等的，你不能只选一个来分析，你的模型必须解释所有的间隔。

After cleaning your data and finding what features are most important, using your model as a predictive tool will only enhance your business decision making.
在清洗你的数据并发现哪些特征是最重要的之后，使用你的模型作为预测工具只会增强你的业务决策。

If you're a mathematician, researcher, or otherwise inclined to understand what your model is really doing, consider choosing PyTorch.
但是，如果你是一位数学家、研究人员或者倾向于理解你的模型真正在做什么，那么就考虑选择PyTorch。

The important thing is to begin iterating as quickly as possible, so you can try out your model with real users early and often.
重要的是尽可能快地开始迭代，这样你就可以尽早且经常性地让实际用户来试用你的模型。