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  2. Attention (machine learning) - Wikipedia

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

    Attention module – this can be a dot product of recurrent states, or the query-key-value fully-connected layers. The output is a 100-long vector w. H 500×100. 100 hidden vectors h concatenated into a matrix c 500-long context vector = H * w. c is a linear combination of h vectors weighted by w.

  3. Transformer (deep learning architecture) - Wikipedia

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

    A non-masked attention module can be thought of as a masked attention module where the mask has all entries zero. As an example of an uncommon use of mask matrix, the XLNet considers all masks of the form P M causal P − 1 {\displaystyle PM_{\text{causal}}P^{-1}} , where P {\displaystyle P} is a random permutation matrix .

  4. 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 ...

  5. Attention Is All You Need - Wikipedia

    en.wikipedia.org/wiki/Attention_Is_All_You_Need

    Scaled dot-product attention & self-attention. The use of the scaled dot-product attention and self-attention mechanism instead of a Recurrent neural network or Long short-term memory (which rely on recurrence instead) allow for better performance as described in the following paragraph. The paper described the scaled-dot production as follows:

  6. Torch (machine learning) - Wikipedia

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

    It is divided into modular objects that share a common Module interface. Modules have a forward() and backward() method that allow them to feedforward and backpropagate , respectively. Modules can be joined using module composites , like Sequential , Parallel and Concat to create complex task-tailored graphs.

  7. 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.

  8. Tensor Processing Unit - Wikipedia

    en.wikipedia.org/wiki/Tensor_Processing_Unit

    In January 2019, Google made the Edge TPU available to developers with a line of products under the Coral brand. The Edge TPU is capable of 4 trillion operations per second with 2 W of electrical power. [44] The product offerings include a single-board computer (SBC), a system on module (SoM), a USB accessory, a mini PCI-e card, and an M.2 card.

  9. Chrome Web Store - Wikipedia

    en.wikipedia.org/wiki/Chrome_Web_Store

    Chrome Web Store was publicly unveiled in December 2010, [2] and was opened on February 11, 2011, with the release of Google Chrome 9.0. [3] A year later it was redesigned to "catalyze a big increase in traffic, across downloads, users, and total number of apps". [4]

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