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  2. PyTorch Lightning - Wikipedia

    en.wikipedia.org/wiki/PyTorch_Lightning

    PyTorch Lightning is an open-source Python library that provides a high-level interface for PyTorch, a popular deep learning framework. [1] It is a lightweight and high-performance framework that organizes PyTorch code to decouple research from engineering, thus making deep learning experiments easier to read and reproduce.

  3. PyTorch - Wikipedia

    en.wikipedia.org/wiki/PyTorch

    In September 2022, Meta announced that PyTorch would be governed by the independent PyTorch Foundation, a newly created subsidiary of the Linux Foundation. [ 24 ] PyTorch 2.0 was released on 15 March 2023, introducing TorchDynamo , a Python-level compiler that makes code run up to 2x faster, along with significant improvements in training and ...

  4. Roofline model - Wikipedia

    en.wikipedia.org/wiki/Roofline_model

    The roofline model is an intuitive visual performance model used to provide performance estimates of a given compute kernel or application running on multi-core, many-core, or accelerator processor architectures, by showing inherent hardware limitations, and potential benefit and priority of optimizations.

  5. Past paper - Wikipedia

    en.wikipedia.org/wiki/Past_paper

    A past paper is an examination paper from a previous year or previous years, usually used either for exam practice or for tests such as University of Oxford, [1] [2] University of Cambridge [3] College Collections. Exam candidates find past papers valuable in test preparation.

  6. Attention (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Attention_(machine_learning)

    As hand-crafting weights defeats the purpose of machine learning, the model must compute the attention weights on its own. Taking analogy from the language of database queries, we make the model construct a triple of vectors: key, query, and value. The rough idea is that we have a "database" in the form of a list of key-value pairs.

  7. Graph neural network - Wikipedia

    en.wikipedia.org/wiki/Graph_neural_network

    Countermeasures such as skip connections [10] [38] (as in residual neural networks), gated update rules [39] and jumping knowledge [40] can mitigate oversmoothing. Modifying the final layer to be a fully-adjacent layer, i.e., by considering the graph as a complete graph , can mitigate oversquashing in problems where long-range dependencies are ...

  8. 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 ...

  9. Capsule neural network - Wikipedia

    en.wikipedia.org/wiki/Capsule_neural_network

    [1] [3] A second, digit capsule layer has one 16-dimensional capsule for each digit (0-9). Dynamic routing connects (only) primary and digit capsule layers. A [32x6x6] x 10 weight matrix controls the mapping between layers. [1] Capsnets are hierarchical, in that each lower-level capsule contributes significantly to only one higher-level capsule ...

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