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embedding    
嵌入

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embedding
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embedding
嵌入

Embed \Em*bed"\ ([e^]m*b[e^]d"), v. t. [imp. & p. p. {Embedded};
p. pr. & vb. n. {Embedding}.] [Pref. em- bed. Cf. {Imbed}.]
To lay as in a bed; to lay in surrounding matter; to bed; as,
to embed a thing in clay, mortar, or sand.
[1913 Webster]

1. One instance of some mathematical object
contained with in another instance, e.g. a {group} which is a
subgroup.

2. ({domain theory}) A {complete partial order} F in
[X -> Y] is an embedding if

(1) For all x1, x2 in X, x1 <= x2 <=> F x1 <= F x2 and

(2) For all y in Y, {x | F x <= y} is {directed}.

("<=" is written in {LaTeX} as {\sqsubseteq}).

(1995-03-27)


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英文字典中文字典相关资料:


  • What are embeddings in machine learning? - GeeksforGeeks
    The goal of embeddings is to capture the semantic meaning and relationships within the data in a way that similar items are closer together in the embedding space
  • Embedding - Wikipedia
    In mathematics, an embedding (or imbedding[1]) is one instance of some mathematical structure contained within another instance, such as a group that is a subgroup
  • What is embedding? - IBM
    What is embedding? Embedding is a means of representing objects like text, images and audio as points in a continuous vector space where the locations of those points in space are semantically meaningful to machine learning (ML) algorithms
  • Embedding (machine learning) - Wikipedia
    In machine learning, embedding is a representation learning technique that maps complex, high-dimensional data into a lower-dimensional vector space of numerical vectors
  • Embeddings: A Deep Dive from Basics to Advanced Concepts
    In this example, the embedding-based similarity is significantly higher than the token-based similarity, reflecting the semantic similarities between the sentences
  • What is Embedding? - Embeddings in Machine Learning Explained - AWS
    Embedding models are algorithms trained to encapsulate information into dense representations in a multi-dimensional space Data scientists use embedding models to enable ML models to comprehend and reason with high-dimensional data
  • Embeddings in Machine Learning - GeeksforGeeks
    Important terms used for Embedding These terms help understand how embeddings represent and organize data in machine learning 1 Vector A vector is a list of numbers representing features or characteristics of data, often showing magnitude and direction Example: In 2D, the vector points 3 steps along the x-axis and 4 steps along the y-axis
  • Embeddings | Machine Learning | Google for Developers
    This course module teaches the key concepts of embeddings, and techniques for training an embedding to translate high-dimensional data into a lower-dimensional embedding vector
  • Getting Started With Embeddings - Hugging Face
    We’re on a journey to advance and democratize artificial intelligence through open source and open science
  • Transformer | 一文带你了解Embedding（从传统嵌入方法到大模型Embedding）
    Embedding不仅适用于文本，还可以应用于图像、音频甚至图数据。 「广义上讲，Embedding是将（任何类型的）数据转换为向量的过程」。 当然，每种模态的Embedding方法都各不相同且独一无二。 在本文介绍的“Embedding”，主要指的是文本Embedding。





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