Showing 4 open source projects for "compilers"

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  • 1
    Ohm

    Ohm

    A library and language for building parsers, interpreters, compilers

    Ohm is a parsing toolkit consisting of a library and a domain-specific language. It allows developers to define grammars and build parsers, interpreters, and compilers with ease, facilitating language design and processing tasks.​
    Downloads: 0 This Week
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  • 2
    magic-string

    magic-string

    Manipulate strings like a wizard

    magic-string is a small JavaScript utility for editing strings while preserving accurate source maps. It is designed for compilers, bundlers, preprocessors, and code transformation tools that need to rewrite source code safely. The library lets developers overwrite, append, prepend, remove, move, and slice sections of text without losing track of where transformed content came from. This makes it especially useful when generated output still needs to map back to the original source for debugging. ...
    Downloads: 0 This Week
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  • 3
    Rollup

    Rollup

    A module bundler for JavaScript

    Rollup is a module bundler for JavaScript which compiles small pieces of code into something larger and more complex, such as a library or application. It uses the new standardized format for code modules included in the ES6 revision of JavaScript, instead of previous idiosyncratic solutions such as CommonJS and AMD. ES modules let you freely and seamlessly combine the most useful individual functions from your favorite libraries. This will eventually be possible natively everywhere, but...
    Downloads: 1 This Week
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  • 4
    ConvNetJS

    ConvNetJS

    Deep learning in Javascript to train convolutional neural networks

    ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in your browser. Open a tab and you're training. No software requirements, no compilers, no installations, no GPUs, no sweat. ConvNetJS is an implementation of Neural networks, together with nice browser-based demos. It currently supports common Neural Network modules (fully connected layers, non-linearities), classification (SVM/Softmax) and Regression (L2) cost functions, ability to specify and train Convolutional Networks that process images, and experimental Reinforcement Learning modules, based on Deep Q Learning. ...
    Downloads: 0 This Week
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