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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]
Designed to enable fast experimentation with deep neural networks, Keras focuses on being user-friendly, modular, and extensible. It was developed as part of the research effort of project ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System), [5] and its primary author and maintainer is François Chollet, a Google engineer
Blue Brain Project, an attempt to create a synthetic brain by reverse-engineering the mammalian brain down to the molecular level. [1] Google Brain, a deep learning project part of Google X attempting to have intelligence similar or equal to human-level. [2] Human Brain Project, ten-year scientific research project, based on exascale ...
The idea of skip-gram is that the vector of a word should be close to the vector of each of its neighbors. The idea of CBOW is that the vector-sum of a word's neighbors should be close to the vector of the word. In the original publication, "closeness" is measured by softmax, but the framework allows other ways to measure closeness.
A word n-gram language model is a purely statistical model of language. It has been superseded by recurrent neural network–based models, which have been superseded by large language models. [12] It is based on an assumption that the probability of the next word in a sequence depends only on a fixed size window of previous words.
In software and systems engineering, a use case is a potential scenario in which a system receives an external request (such as user input) and responds to it. A use case is a list of actions or event steps typically defining the interactions between a role (known in the Unified Modeling Language (UML) as an actor) and a system to achieve a goal.
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
Caselaw Access Project All official, book-published state and federal United States case law — every volume or case designated as an official report of decisions by a court within the United States. Cleaned and normalized text ~10,000 Json NLP, linguistics 2022 [78] A. Aizman; S. Chapman; J. Cushman; K. Dulin; H. Eidolon; et al.