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  2. Document Object Model - Wikipedia

    en.wikipedia.org/wiki/Document_Object_Model

    To render a document such as a HTML page, most web browsers use an internal model similar to the DOM. The nodes of every document are organized in a tree structure, called the DOM tree, with the topmost node named as "Document object". When an HTML page is rendered in browsers, the browser downloads the HTML into local memory and automatically ...

  3. Word embedding - Wikipedia

    en.wikipedia.org/wiki/Word_embedding

    In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis.Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]

  4. Front-end web development - Wikipedia

    en.wikipedia.org/wiki/Front-end_web_development

    JavaScript is an event-based imperative programming language (as opposed to HTML's declarative language model) that is used to transform a static HTML page into a dynamic interface. JavaScript code can use the Document Object Model (DOM), provided by the HTML standard, to manipulate a web page in response to events, like user input.

  5. Object Linking and Embedding - Wikipedia

    en.wikipedia.org/wiki/Object_Linking_and_Embedding

    The main benefit of OLE is to add different kinds of data to a document from different applications, like a text editor and an image editor. This creates a Compound File Binary Format document and a master file to which the document makes reference. Changes to data in the master file immediately affect the document that references it.

  6. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    The word with embeddings most similar to the topic vector might be assigned as the topic's title, whereas far away word embeddings may be considered unrelated. As opposed to other topic models such as LDA, top2vec provides canonical ‘distance’ metrics between two topics, or between a topic and another embeddings (word, document, or ...

  7. Bag-of-words model - Wikipedia

    en.wikipedia.org/wiki/Bag-of-words_model

    It disregards word order (and thus most of syntax or grammar) but captures multiplicity. The bag-of-words model is commonly used in methods of document classification where, for example, the (frequency of) occurrence of each word is used as a feature for training a classifier. [1] It has also been used for computer vision. [2]

  8. eRuby - Wikipedia

    en.wikipedia.org/wiki/ERuby

    Embedded Ruby (also shortened as ERB) is a templating system that embeds Ruby into a text document. It is often used to embed Ruby code in an HTML document, similar to ASP and JSP , and PHP and other server-side scripting languages.

  9. Latent space - Wikipedia

    en.wikipedia.org/wiki/Latent_space

    Here are some commonly used embedding models: Word2Vec: [4] Word2Vec is a popular embedding model used in natural language processing (NLP). It learns word embeddings by training a neural network on a large corpus of text. Word2Vec captures semantic and syntactic relationships between words, allowing for meaningful computations like word analogies.

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