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  2. Comparison of raster-to-vector conversion software - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_raster-to...

    Super Vectorizer Effectmatrix Ltd. 2013 2016 (version 1.6.6) $29.99 Proprietary: macOS AutoTrace: Martin Weber 1998 2024 (version 0.31.10) Free GPL-2.0-or-later: Cross-platform, POSIX (mainly Unix-like) VTracer: Tsang Hao Fung 2020 2022 (version 0.5.0) Free MIT: Cross-platform (command-line version) Browsers with WebAssembly support (web version)

  3. Image tracing - Wikipedia

    en.wikipedia.org/wiki/Image_tracing

    Erase stray marks and fill in lines and areas. Corel advice: Put the image on a light table, cover it with vellum ( tracing paper ), and then manually ink the desired outlines. Then scan the vellum and use an automated raster-to-vector conversion program on that scan.

  4. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    e. Word2vec is a technique in natural language processing (NLP) for obtaining vector representations of words. These vectors capture information about the meaning of the word based on the surrounding words. The word2vec algorithm estimates these representations by modeling text in a large corpus. Once trained, such a model can detect synonymous ...

  5. Feature hashing - Wikipedia

    en.wikipedia.org/wiki/Feature_hashing

    Feature hashing. In machine learning, feature hashing, also known as the hashing trick (by analogy to the kernel trick ), is a fast and space-efficient way of vectorizing features, i.e. turning arbitrary features into indices in a vector or matrix. [1] [2] It works by applying a hash function to the features and using their hash values as ...

  6. Automatic vectorization - Wikipedia

    en.wikipedia.org/wiki/Automatic_vectorization

    Automatic vectorization. Automatic vectorization, in parallel computing, is a special case of automatic parallelization, where a computer program is converted from a scalar implementation, which processes a single pair of operands at a time, to a vector implementation, which processes one operation on multiple pairs of operands at once.

  7. Generative pre-trained transformer - Wikipedia

    en.wikipedia.org/wiki/Generative_pre-trained...

    History Initial developments. Generative pretraining (GP) was a long-established concept in machine learning applications. It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.

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