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  2. Template:Machine learning - Wikipedia

    en.wikipedia.org/wiki/Template:Machine_learning

    Template:Machine learning/styles.css This page was last edited on 11 October 2024, at 05:09 (UTC). Text is available under the Creative Commons ...

  3. Microsoft PowerPoint - Wikipedia

    en.wikipedia.org/wiki/Microsoft_PowerPoint

    In contemporary operation, PowerPoint is used to create a file (called a "presentation" or "deck") containing a sequence of pages (called "slides" in the app) which usually have a consistent style (from template masters), and which may contain information imported from other apps or created in PowerPoint, including text, bullet lists, tables ...

  4. Machine learning - Wikipedia

    en.wikipedia.org/wiki/Machine_learning

    Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]

  5. Artificial Intelligence Markup Language - Wikipedia

    en.wikipedia.org/wiki/Artificial_Intelligence...

    The XML dialect called AIML was developed by Richard Wallace and a worldwide free software community between 1995 [citation needed] and 2002. AIML formed the basis for what was initially a highly extended Eliza called "A.L.I.C.E." ("Artificial Linguistic Internet Computer Entity"), which won the annual Loebner Prize Competition in Artificial Intelligence [3] three times, and was also the ...

  6. Recurrent neural network - Wikipedia

    en.wikipedia.org/wiki/Recurrent_neural_network

    Recurrent neural networks (RNNs) are a class of artificial neural network commonly used for sequential data processing. Unlike feedforward neural networks, which process data in a single pass, RNNs process data across multiple time steps, making them well-adapted for modelling and processing text, speech, and time series.

  7. Feature learning - Wikipedia

    en.wikipedia.org/wiki/Feature_learning

    Feature learning is intended to result in faster training or better performance in task-specific settings than if the data was input directly (compare transfer learning). [1] In machine learning (ML), feature learning or representation learning [2] is a set of techniques that allow a system to automatically discover the representations needed ...