Showing 4 open source projects for "learning system"

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  • 1
    The HaskellR project

    The HaskellR project

    The full power of R in Haskell

    ...HaskellR allows Haskell functions to seamlessly call R functions and vice versa. It provides the Haskell programmer with the full breadth of existing R libraries and extensions for numerical computation, statistical analysis and machine learning. Optionally, pass in the --nix flag to all commands if you have the Nix package manager installed. Nix can populate a local build environment including all necessary system dependencies without touching your global filesystem. Use it as a cross-platform alternative to Docker.
    Downloads: 1 This Week
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  • 2
    Scotty

    Scotty

    Haskell web framework inspired by Ruby's Sinatra, using WAI and Warp

    Scotty is a lightweight Haskell web framework inspired by Ruby’s Sinatra. It allows developers to build RESTful web applications and APIs with minimal boilerplate. Scotty is built on top of the WAI (Web Application Interface) and Warp server, making it fast and scalable. It emphasizes simplicity and ease of use, making it ideal for small- to medium-sized services or for developers learning web programming in Haskell.
    Downloads: 0 This Week
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  • 3
    IHaskell

    IHaskell

    A Haskell kernel for the Jupyter project

    IHaskell is a kernel for the Jupyter project, which allows you to use Haskell inside Jupyter frontends (including the console and notebook). It currently supports GHC 8.0 through 9.0. For GHC 7.10 support please use the GHC7 tag. For a tour of some IHaskell features, check out the demo Notebook. More example notebooks are available on the wiki. The wiki also has more extensive documentation of IHaskell features. IHaskell is based upon the IPython architecture and frontend, so learning about...
    Downloads: 0 This Week
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  • 4
    HLearn

    HLearn

    Homomorphic machine learning

    HLearn is a Haskell-based machine learning library focused on composability, algebraic structure, and performance. It provides a functional approach to building machine learning algorithms by leveraging algebraic properties such as monoids and groups. This allows for parallel, incremental, and distributed computation in a mathematically consistent way. HLearn aims to provide implementations of common algorithms like k-means, naive Bayes, and others while maintaining the expressiveness and...
    Downloads: 0 This Week
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