Showing 4 open source projects for "machine learning"

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  • 1
    The Julia Programming Language

    The Julia Programming Language

    High-level, high-performance dynamic language for technical computing

    Julia is a fast, open source high-performance dynamic language for technical computing. It can be used for data visualization and plotting, deep learning, machine learning, scientific computing, parallel computing and so much more. Having a high level syntax, Julia is easy to use for programmers of every level and background. Julia has more than 2,800 community-registered packages including various mathematical libraries, data manipulation tools, and packages for general purpose computing. ...
    Downloads: 16 This Week
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  • 2
    Avocado programming language

    Avocado programming language

    Avocado Polish programming language

    The Avocado language is compiled and currently allows for the creation of console applications. Work on Avocado and the integrated development environment (IDE) began on February 19, 2025. A unique feature of this language is the ability to write commands in Polish and English, compiling code into .exe format. The language is freely available for commercial and non-commercial projects. The Avocado source code is available under the MIT License on GitHub. Avocado is transpiled to Free...
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    Downloads: 14 This Week
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  • 3
    Pipelines

    Pipelines

    An experimental programming language for data flow

    ...Unlike other languages for defining data flow, the Pipeline language requires the implementation of components to be defined separately in the Python scripting language. This allows the details of implementations to be separated from the structure of the pipeline while providing access to thousands of active libraries for machine learning, data analysis, and processing.
    Downloads: 0 This Week
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  • 4
    Edward

    Edward

    A probabilistic programming language in TensorFlow

    ...It is a testbed for fast experimentation and research with probabilistic models, ranging from classical hierarchical models on small data sets to complex deep probabilistic models on large data sets. Edward fuses three fields, Bayesian statistics and machine learning, deep learning, and probabilistic programming. Edward is built on TensorFlow. It enables features such as computational graphs, distributed training, CPU/GPU integration, automatic differentiation, and visualization with TensorBoard. Expectation-Maximization, pseudo-marginal and ABC methods, and message passing algorithms.
    Downloads: 2 This Week
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