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  2. Transformer (deep learning architecture) - Wikipedia

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

    Multiheaded attention, block diagram. Exact dimension counts within a multiheaded attention module. One set of (,,) matrices is called an attention head, and each layer in a transformer model has multiple attention heads. While each attention head attends to the tokens that are relevant to each token, multiple attention heads allow the model to ...

  3. Residual neural network - Wikipedia

    en.wikipedia.org/wiki/Residual_neural_network

    A basic block is the simplest building block studied in the original ResNet. [1] This block consists of two sequential 3x3 convolutional layers and a residual connection. The input and output dimensions of both layers are equal. Block diagram of ResNet (2015). It shows a ResNet block with and without the 1x1 convolution.

  4. VisSim - Wikipedia

    en.wikipedia.org/wiki/VisSim

    VisSim is a visual block diagram program for the simulation of dynamical systems and model-based design of embedded systems, with its own visual language.It is developed by Visual Solutions of Westford, Massachusetts.

  5. PlantUML - Wikipedia

    en.wikipedia.org/wiki/PlantUML

    PlantUML is an open-source tool allowing users to create diagrams from a plain text language. Besides various UML diagrams, PlantUML has support for various other software development related formats (such as Archimate, Block diagram, BPMN, C4, Computer network diagram, ERD, Gantt chart, Mind map, and WBD), as well as visualisation of JSON and YAML files.

  6. Block diagram - Wikipedia

    en.wikipedia.org/wiki/Block_diagram

    A block diagram is a diagram of a system in which the principal parts or functions are represented by blocks connected by lines that show the relationships of the blocks. [1] They are heavily used in engineering in hardware design , electronic design , software design , and process flow diagrams .

  7. Attention (machine learning) - Wikipedia

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

    Encoder self-attention, block diagram Encoder self-attention, detailed diagram. Self-attention is essentially the same as cross-attention, except that query, key, and value vectors all come from the same model. Both encoder and decoder can use self-attention, but with subtle differences.

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