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  2. Transformer (deep learning architecture) - Wikipedia

    en.wikipedia.org/wiki/Transformer_(deep_learning...

    For many years, sequence modelling and generation was done by using plain recurrent neural networks (RNNs). A well-cited early example was the Elman network (1990). In theory, the information from one token can propagate arbitrarily far down the sequence, but in practice the vanishing-gradient problem leaves the model's state at the end of a long sentence without precise, extractable ...

  3. LinkedIn Learning - Wikipedia

    en.wikipedia.org/wiki/LinkedIn_Learning

    LinkedIn Learning was founded as Lynda.com in 1995 in Ojai, California, as online support for the books and classes of Lynda Weinman, a special effects animator and multimedia professor who founded a digital arts school with her husband, artist Bruce Heavin.

  4. Learning management system - Wikipedia

    en.wikipedia.org/wiki/Learning_management_system

    A learning management system (LMS) or virtual learning environment (VLE) is a software application for the administration, documentation, tracking, reporting, automation, and delivery of educational courses, training programs, materials or learning and development programs. [1] The learning management system concept emerged directly from e ...

  5. Instructure - Wikipedia

    en.wikipedia.org/wiki/Instructure

    Instructure Holdings, Inc. is an educational technology company based in Salt Lake City, Utah, United States.It is the developer and publisher of Canvas, a web-based learning management system (LMS), and Mastery Connect, an assessment management system.

  6. Stochastic gradient descent - Wikipedia

    en.wikipedia.org/wiki/Stochastic_gradient_descent

    Informally, this increases the learning rate for sparser parameters [clarification needed] and decreases the learning rate for ones that are less sparse. This strategy often improves convergence performance over standard stochastic gradient descent in settings where data is sparse and sparse parameters are more informative.

  7. Backpropagation - Wikipedia

    en.wikipedia.org/wiki/Backpropagation

    The goal of any supervised learning algorithm is to find a function that best maps a set of inputs to their correct output. The motivation for backpropagation is to train a multi-layered neural network such that it can learn the appropriate internal representations to allow it to learn any arbitrary mapping of input to output.

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