embedding

简明释义

英[ɪm'bedɪŋ]美[ɪmˈbedɪŋ]

n. 记者随军

v. 把（物体）嵌入；派遣（战地记者等）；使（观点）深深植入；嵌进（短语）；插入（代码）（embed 的现在分词）

英英释义

单词用法

同义词

反义词

例句

1.Embedding a scripting language offers a number of advantages.

嵌入一种脚本语言可以带来很多的好处。

2.This leaves open the option of embedding more vocabularies, such as xhtml:.

这样就为嵌入其他的词汇表留了余地，如xhtml: 。

3.But DCMI is particularly well suited for embedding within XML generally.

不过一般而言dcmi尤其适合于嵌入在XML中。

4.Support for embedding widgets in ListGrid cells

可以在ListGrid单元格中嵌入widget。

5.Others are embedding this kind of technology in more obvious applications.

还有一些基于这个技术的更显而易见的应用。

6.The key ingredients of a great Flash embedding method.

好的flash嵌入方法的关键。

7.Increase space utilization by embedding less free space.

通过嵌入更少的空闲空间提高空间利用率。

8.Embedding minimizes errors because the end user has only one file.

嵌入减少了错误，因为最终用户只有一个文件。

9.The model uses word embedding to represent words in a continuous vector space.

该模型使用词汇嵌入来在连续向量空间中表示单词。

10.The embedding of user preferences helps tailor personalized content.

用户偏好的嵌入有助于定制个性化内容。

11.The embedding of images allows for better similarity comparisons in machine learning.

图像的嵌入使得在机器学习中进行更好的相似性比较。

12.We can use embedding techniques to improve the performance of our recommendation system.

我们可以使用嵌入技术来提高推荐系统的性能。

13.In deep learning, embedding layers help to convert categorical variables into numerical format.

在深度学习中，嵌入层有助于将分类变量转换为数值格式。

作文

In the realm of technology and data science, the concept of embedding plays a pivotal role in how we understand and utilize information. Embedding, in its simplest form, refers to the process of transforming high-dimensional data into a lower-dimensional space while preserving its significant properties. This technique is widely used in various applications, including natural language processing, image recognition, and even recommendation systems.One of the most prominent examples of embedding can be found in the field of natural language processing (NLP). Here, words are converted into numerical vectors through a process known as word embedding. This allows computers to understand the semantic meaning of words based on their context. For instance, the words 'king' and 'queen' may have similar vectors because they share common attributes related to royalty. By using embedding techniques like Word2Vec or GloVe, we can capture these relationships and improve the performance of various NLP tasks such as sentiment analysis and machine translation.Similarly, in the domain of image processing, embedding is employed to represent images in a more manageable format. Convolutional neural networks (CNNs) are often used to extract features from images, which are then transformed into lower-dimensional vectors or embeddings. These embeddings can be utilized for image classification, object detection, and even facial recognition. By reducing the complexity of the data, embedding not only enhances computational efficiency but also aids in achieving better accuracy in these tasks.Moreover, recommendation systems heavily rely on embedding techniques to provide personalized suggestions to users. By embedding user preferences and item characteristics into a shared vector space, systems can calculate similarities between users and items effectively. For example, if a user enjoys action movies, the system can identify other movies with similar embeddings and recommend them accordingly. This approach significantly improves user experience and engagement by tailoring content to individual tastes.The importance of embedding extends beyond just technical applications; it also has profound implications in understanding human cognition. Researchers have begun to explore how embedding can model the way humans perceive and categorize information. By analyzing how different concepts are embedded in our minds, scientists can gain insights into learning processes and cognitive biases. This interdisciplinary approach bridges the gap between artificial intelligence and human psychology, leading to advancements in both fields.In conclusion, the concept of embedding is a fundamental aspect of modern technology that enables us to process and analyze complex data efficiently. Whether in language, images, or user preferences, embedding techniques facilitate a deeper understanding of information by transforming it into a more usable form. As we continue to innovate and expand the boundaries of technology, the role of embedding will undoubtedly become even more significant, shaping the future of data science and artificial intelligence.嵌入在技术和数据科学领域发挥着关键作用，帮助我们理解和利用信息。嵌入的简单形式是将高维数据转化为低维空间，同时保留其重要特性。这种技术广泛应用于自然语言处理、图像识别甚至推荐系统等各种应用。在自然语言处理（NLP）领域，嵌入的一个显著例子是将单词转换为数字向量的过程，称为词嵌入。这使得计算机能够根据上下文理解单词的语义。例如，单词“王”和“后”可能具有相似的向量，因为它们共享与皇室相关的共同属性。通过使用像Word2Vec或GloVe这样的嵌入技术，我们可以捕捉这些关系，并提高各种NLP任务的性能，如情感分析和机器翻译。同样，在图像处理领域，嵌入被用来以更易管理的格式表示图像。卷积神经网络（CNN）通常用于从图像中提取特征，然后将其转化为低维向量或嵌入。这些嵌入可用于图像分类、物体检测甚至面部识别。通过降低数据的复杂性，嵌入不仅提高了计算效率，还帮助在这些任务中实现更好的准确性。此外，推荐系统严重依赖嵌入技术，为用户提供个性化建议。通过将用户偏好和项目特征嵌入到共享向量空间，系统可以有效地计算用户与项目之间的相似性。例如，如果用户喜欢动作电影，系统可以识别其他具有相似嵌入的电影并相应推荐。这种方法通过根据个人喜好定制内容，显著改善了用户体验和参与度。嵌入的重要性不仅限于技术应用；它在理解人类认知方面也具有深远的影响。研究人员开始探索如何通过嵌入来模拟人类感知和分类信息的方式。通过分析不同概念如何在我们脑中被嵌入，科学家可以获得有关学习过程和认知偏见的洞察。这种跨学科的方法弥合了人工智能与人类心理学之间的差距，推动了两个领域的进步。总之，嵌入的概念是现代技术的基本方面，使我们能够高效地处理和分析复杂数据。无论是在语言、图像还是用户偏好中，嵌入技术通过将信息转化为更可用的形式，促进了对信息的更深理解。随着我们继续创新并拓展技术的边界，嵌入的角色无疑将变得更加重要，塑造数据科学和人工智能的未来。