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  2. Django (web framework) - Wikipedia

    en.wikipedia.org/wiki/Django_(web_framework)

    Django (/ ˈ dʒ æ ŋ ɡ oʊ / JANG-goh; sometimes stylized as django) [6] is a free and open-source, Python-based web framework that runs on a web server. It follows the model–template–views (MTV) architectural pattern .

  3. IDEF0 - Wikipedia

    en.wikipedia.org/wiki/IDEF0

    IDEF0 Diagram Example. IDEF0, a compound acronym ("Icam DEFinition for Function Modeling", where ICAM is an acronym for "Integrated Computer Aided Manufacturing"), is a function modeling methodology for describing manufacturing functions, which offers a functional modeling language for the analysis, development, reengineering and integration of information systems, business processes or ...

  4. React (software) - Wikipedia

    en.wikipedia.org/wiki/React_(software)

    The introduction of React Hooks with React 16.8 in February 2019 allowed developers to manage state and lifecycle behaviors within functional components, reducing the reliance on class components. This trend aligns with the broader industry movement towards functional programming and modular design.

  5. Input hypothesis - Wikipedia

    en.wikipedia.org/wiki/Input_hypothesis

    Comprehensible input hypothesis. The input hypothesis, also known as the monitor model, is a group of five hypotheses of second-language acquisition developed by the linguist Stephen Krashen in the 1970s and 1980s. Krashen originally formulated the input hypothesis as just one of the five hypotheses, but over time the term has come to refer to ...

  6. Multilayer perceptron - Wikipedia

    en.wikipedia.org/wiki/Multilayer_perceptron

    The MLP consists of three or more layers (an input and an output layer with one or more hidden layers) of nonlinearly-activating nodes. Since MLPs are fully connected, each node in one layer connects with a certain weight w i j {\displaystyle w_{ij}} to every node in the following layer.

  7. Hidden Markov model - Wikipedia

    en.wikipedia.org/wiki/Hidden_Markov_model

    Figure 1. Probabilistic parameters of a hidden Markov model (example) X — states y — possible observations a — state transition probabilities b — output probabilities. In its discrete form, a hidden Markov process can be visualized as a generalization of the urn problem with replacement (where each item from the urn is returned to the original urn before the next step). [7]