Showing 4 open source projects for "together"

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    SpaceVim

    SpaceVim

    A community-driven modular vim/neovim distribution

    SpaceVim is a community-driven modular Vim distribution. It manages collections of plugins in layers, which help to collect related packages together to provide IDE-like features. First of all, you need to install Vim or Neovim, preferably with +python3 support enabled. Also, you need to have git and curl installed in your system, which is needed for downloading plugins and fonts. If you are using a terminal emulator, you will need to set the font in the terminal configuration. ...
    Downloads: 0 This Week
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  • 2

    lglicua

    Developer Assistant for PUC-Rio Tecgraf sci/tech toolkits on GNU/Linux

    This project is an assistant for developers wanting to use one of Lua 5.1, 5.2, 5.3 and/or 5.4, together with the PUC-Rio Tecgraf scientific/technical toolkit projects, on a range of GNU/Linux distributions. The Tecgraf projects are hosted on SourceForge: "im" (Image Manipulation) <https://sourceforge.net/projects/imtoolkit/> "cd" (Canvas Draw) <https://sourceforge.net/projects/canvasdraw/> "iup" (Toolkit for GUIs) <https://sourceforge.net/projects/iup/> Lua also comes out of PUC-Rio, and is a small, portable, fast, powerful, readable, flexible and free scripting/extension language. ...
    Downloads: 1 This Week
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  • 3
    Fennel

    Fennel

    Lua Lisp Language

    Fennel is a programming language that brings together the speed, simplicity and reach of Lua with the flexibility of a lisp syntax and macro system. Full Lua compatibility: Easily call any Lua function or library from Fennel and vice-versa. Zero overhead: Compiled code should be just as efficient as hand-written Lua. Compile-time macros: Ship compiled code with no runtime dependency on Fennel.
    Downloads: 0 This Week
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  • 4
    ResNeXt

    ResNeXt

    Implementation of a classification framework

    ...Instead of simply increasing depth or width, ResNeXt introduces a new dimension called cardinality, which refers to the number of parallel transformation paths (i.e. the number of “branches”) that are aggregated together. Each branch is a small transformation (e.g. bottleneck block) and their outputs are summed—this enables richer representation without excessive parameter blowup. The design is modular and homogeneous, making it relatively easy to scale (by tuning cardinality, width, depth) and adopt in existing residual frameworks. The official repository offers a Torch (Lua) implementation with code for training, evaluation, and pretrained models on ImageNet. ...
    Downloads: 0 This Week
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