Examples of using 你的模型 in Chinese and their translations into English
{-}
-
Political
-
Ecclesiastic
-
Programming
如果一切顺利，你的模型将能做出很棒的预测。
Validate命令检查你的模型的语法和逻辑是否正确。
据此设计你的模型。
Design based on your model.不断回测你的模型.
Constantly iterate on your model.改变你的模型处于布局自动反映出来。
Changes to your model are reflected automatically in layout.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）.
Plan for how often your model will need to be retrained with updated data(e.g. perhaps you will retrain nightly or weekly).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。
But you can't use that to prove your model is always as accurate as its cross-validation score, Ortiz explains.通过技巧和诀窍，你将学会如何让你的模型的学习变得更有效率，变得更有创造力。
Through tips and tricks, you will understand how to make your models learn more efficiently and become more creative.你不需要担心底层实现或者发布你的模型，我们可扩展的云系统会帮你完成这些。
You don't have to worry about the underlying infrastructure or deploying your models, our scalable cloud does this for you.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.
So as your model is close to reality, and it converges with feelings, you often don't know it's there.一旦你建立你的模型，你可以将它们放置在谷歌地球，它们发布到3D模型库，或打印硬拷贝。
After you have built your models, you can place them in Google Earth, post them to the 3D Warehouse, or print hard copies.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。
If you're a mathematician, researcher, or otherwise inclined to understand what your model is really doing, consider choosing PyTorch.修改你的模型，只要可能就使用1x1的CNN层，它的位置对提高性能很有帮助。
Modify your models to use 1x1 CNN's layers where it is possible, the locality is great for performance.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。
Getting your model just right will become much more complicated initially if the walls of the 解释和理解你的模型，以确保你是在获取信息而不是噪音。
Interpret and understand your models, to make sure you are actually capturing information and not noise.一旦你定义好你的模型，你需要告诉Django你将要使用这些模型。
Once you have defined your models, you need to tell Django you're going to use those 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。
And of course if Microsoft is your model, you shouldn't be looking for companies that hope to win by writing great software.这就是为什么你的模型会有更多的数据点而不是更少的数据点。
This is why your models will be better with more data points rather than fewer.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。
If not, your model will degrade over time and won't perform as well, leaving 如何解释并理解你的模型，以确保模型学习的是特征信息而不是噪音.
Interpret and understand your models, to make sure you are actually capturing information and not noise.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。
As your model is in production, its important to update your model periodically, depending on how often you receive new data.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。
And if your model is subscription, you must know 我们还收录了具有新技术(SOTA)结果的论文，供你浏览并改进你的模型。
We have also included papers with state-of-the-art(SOTA)results for you to go through and improve your models.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。
Just do not forget that if your model is going to a business meeting, curls and colorful strands are completely irrelevant.如果要对此做详细解释的话，需要进行更多的数学计算，而你应该把这一点当作为一个准则，让你的模型尽可能得简单。
A detailed explanation requires more math, but as a rule,you should keep your models as simple as possible.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。
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.Adams说，“这是生物学告诉我们的故事-你为什么不回过头来重新思考你的模型。
Here's the story that the biology is telling us-why don't you glaciologists go back and rethink your models.”.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。
And in statistics all intervals are created equal,you don't pick only one for analysis, your model has to explain all intervals.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。
After cleaning 
