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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? - 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
  • 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
  • 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
  • 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
  • What are embeddings? | Learning Center - Cloudflare
    Adding more dimensions to vectors Even more multi-dimensional vectors How do embeddings work? Embedding is the process of creating vectors using deep learning How neural networks create embeddings Neural networks are deep learning models that imitate the architecture of the human brain How are embeddings used in large language
  • A Complete Guide to Embeddings: Techniques, Alternatives, Drift - Aporia
    Each type of embedding has its own properties and techniques for creating them Throughout this guide, we’ll focus on the first two embeddings, which are most commonly used
  • 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: Embedding space and static embeddings
    An embedding is a vector representation of data in embedding space Generally speaking, a model finds potential embeddings by projecting the high-dimensional space of initial data vectors into





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