LABELED DATA - 翻译成中文

• 标记数据
• 标签数据
• 标注的数据

在 英语 中使用 Labeled data 的示例及其翻译为 中文

In order to do this, we must be able to map any given text to a particular topic, which requires massive amounts of labeled data.
为了做到这一点，我们必须首先能够将任何文本与某一特定话题相关联，而这需要大量的标签数据。

In this scenario, you are providing a computer program with labeled data.
我们还可以想象您正在提供一个带有标记数据的计算机程序。

The first way is to go out there and amass a giant stockpile of labeled data on their own.
第一种方法是走出去，自己收集大量的标签数据。

In fact, thanks to its ability to train via simulation, the need for labeled data is removed altogether.
且它的另一个优点是可以通过模拟来训练它，这完全消除了对标记数据的需求。

And there will be new service-based companies that will outsource labeling to low-cost countries, as well as create labeled data through synthetic means.
而且未来也将会产生新的服务型公司，这些公司将标注任务外包给低成本国家，以及通过合成手段来创建标签数据。

These days, nearly all AI-based products in our lives rely on“deep neural networks” that automatically learn to process labeled data.
如今，我们生活中几乎所有基于人工智能的产品都依赖于有自主学习并标记数据能力的“深度神经网络”。

And there's another area of language use that also has plentiful labeled data: machine translation.
语言使用的另一个领域也有大量标记数据：机器翻译。

One can always try to get more labeled data, but this can be expensive.
人们总是可以尝试获取更多的已标注数据，但是这样做成本往往很高。

With so little labeled data, it is a tedious and slow process for data scientists to build machine learning models in most all enterprises.
用这么少的标注数据，对于所有企业中的数据科学家来说，建立机器学习模型都是一个单调乏味的过程。

Often, the practical answer is to work our how to get more labeled data as quickly as you can.
通常，实际的解决方案是如何尽快地获得更多的已标注数据。

We also need labeled data to test our ideas using deep learning.
我们还需要有标签的数据来测试我们的想法能否利用深度学习来实现。

The labeled data set is the teacher that will train you to understand patterns in the data..
标记的数据集是教师，它将训练您理解数据中的模式。

Because of Moore's Law and the internet, we now have enough labeled data and computation to enable ML to create remarkable software.
得益于摩尔定律和互联网，我们现在有足够的标记数据和大量的计算使得机器学习创造卓越的软件。

If you have labeled data, it's a supervised learning problem.
如果你拥有的是带标签的数据，那么这就是一个监督学习问题。

In this case, some labeled data specific to the target domain remains necessary in order to induce an objective predictive model for the target domain.
在这种情况下，一些特定于目标域的标记数据仍然是必需的，以便归纳目标域的客观预测模型。

This ANN is said to have learned from several examples(labeled data) and from its mistakes(error propagation).
该人工神经网络可以说从几个样本（标注数据）和其错误（误差传播）中得到了学习。

Without labeled data, it couldn't recognize its own improvements so it wouldn't know to stick with each improvement along the way.
如果没有标记的数据，它就无法识别自己的改进，因此它就不知道如何坚持每一个改进。

Let's say this labeled data consists of pictures of apples and oranges, respectively.
假设这个标签数据分别由苹果和橘子的图片组成。

The expected output is called a label, and the data is‘labeled data'.
预期输出称为标签，而输入数据就是“打了标记的数据”。

Relationships discovered in this paper can be used to build more effective visual systems that will require less labeled data and lower computational costs.
在本文中发现的关系可以用来构建更有效的视觉系统，这个系统将需要更少的标记数据和更低的计算成本。