标记数据 - 翻译成英语

labeled data
标记 数据
tagged data
标签 数据
标记 数据
labelled data
标记 数据
labeling data
标记 数据
tag data
标签 数据
标记 数据
label data
标记 数据

在 中文 中使用 标记数据 的示例及其翻译为 英语

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使用一个(15,7)Reed-Solomon码,标记数据的112位被冗余编码以生成240个编码位。
Using a(15, 7) Reed-Solomon code, 112 bits of tag data are redundantly encoded to produce 240 encoded bits.
不到三分之一的高管表示,标记数据是他们2019年业务的优先事项。
Less than one-third of executives say labeling data is a priority for their business in 2019.
由于监督学习是最常用的机器学习类型,当人们说“数据”时,它们主要是指标记数据
Since supervised learning ist the most commonly used type of Machine Learning, when people say“data”, they mostly mean labelled data.
转移学习,其中先前针对具有丰富可用标记数据的问题进行训练的模型针对具有有限标记数据的新问题而定制。
Transfer learning, where models previously trained for a problem with copious available labeled data are tailored for a new problem with limited labeled data.
今天的不同之处在于计算能力的丰富性,以及大量标记数据的存在。
The difference today is the abundance of computational power, and the existence of large bodies of tagged data.
在此数据流程图示例中,标记数据时,多次使用“细节”一词。
In this Data Flow Diagram example, the word"details" is used many times when labeling data.
为了检测到标记边界,每个标记数据都必须包含一个标识图案,并且这些图案必须被冗余编码以允许可靠的检测。
To allow tag boundaries to be detected, each tag data must contain a marker pattern, and these must be redundantly encoded to allow reliable detection.
特别是在监督学习的情况下,这意味着你需要大量(非常昂贵的)已标记数据
Especially in the case of supervised learning, this means that you need a lot of(highly expensive) labelled data.
它们是大、且极其复杂的神经网络,使用少量的标记数据和更多的未标记数据。
They are large, extremely complex neural networks that use small amounts of labeled data and much larger amounts of unlabeled data..
全新推出的集成式分析引擎能够帮助企业快速扫描和标记数据,确保敏感或风险信息得到妥善管理和保护。
The Integrated Classification Engine lets organizations quickly scan and tag data to ensure sensitive or risky information is properly managed and protected.
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。
Even more notable is the fact that it can be trained via simulation, which eliminates the need for labeled data entirely.
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。
In this scenario, you are providing a computer program with labeled data.
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。
In fact, thanks to its ability to train via simulation, the need for labeled data is removed altogether.
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”。
These days, nearly all AI-based products in our lives rely on“deep neural networks” that automatically learn to process labeled data.
语言使用的另一个领域也有大量标记数据:机器翻译。
And there's another area of language use that also has plentiful labeled data: machine translation.
目前研究人员面临的挑战主要是大多数神经网络都是通过手工标记数据的方式进行。
One challenge the researchers had to address is that most neural networks are trained using data labeled by hand.
监督式机器学习通常需要人类专家标记数据,然后训练算法来预测这些标记。
Supervised machine learning typically requires human experts to label data and then train an algorithm to predict those labels..
使用一个(15,7)Reed-Solomon码,标记数据的112位被冗余编码以生成240个编码位。
Using a(15, 7) Reed-Solomon code, 112 bits of tag data are redundantly encoded to produce 240.
但是,训练这些模型需要大量标记数据,并且根据手头的任务,必要的语料库有时会供不应求。
But training these models requires lots of labeled data, and depending on the task at hand, the necessary corpora are sometimes in short supply.
标记数据的时间频率在4到90分钟之间,记录时间为14到443天。
The time frequency of the tagging data was between 4 and 90 minutes, and the fish were tagged from 14 to 443 days.
结果: 74, 时间: 0.023

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