Showing 4 open source projects for "python q learning"

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  • 1
    cheat.sh

    cheat.sh

    The only cheat sheet you need

    cheat.sh is a compact, network-accessible cheat-sheet service that serves concise examples and usage notes for hundreds of shell commands, programming languages, and tools via a simple HTTP interface. You can query it from the terminal (for example curl cht.sh/rsync or curl cheat.sh/ls) or browse the web front page; it also supports a shorthand hostname (cht.sh) and provides both online and standalone/local installation modes. The repository contains the server and client code, instructions...
    Downloads: 4 This Week
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  • 2
    CineCLI

    CineCLI

    CineCLI is a cross-platform command-line movie browser

    CineCLI is a command-line utility designed to help movie lovers quickly browse, search, and access film information from the terminal without needing a graphical interface. It connects to popular online movie databases to fetch metadata such as titles, release dates, ratings, genres, casts, posters, and plot summaries, presenting all of that in a concise, text-friendly format suitable for terminals or scripts. Users can search by keyword, year, or exact title and then drill into detailed...
    Downloads: 1 This Week
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  • 3
    q - Text as Data

    q - Text as Data

    Run SQL directly on CSV or TSV files

    q is a command line tool that allows direct execution of SQL-like queries on CSVs/TSVs (and any other tabular text files). q treats ordinary files as database tables, and supports all SQL constructs, such as WHERE, GROUP BY, JOINs etc. It supports automatic column name and column type detection, and provides full support for multiple encodings. q fully supports all types of encoding. Use -e data-encoding to set the input data encoding, -Q query-encoding to set the query encoding, and use -E...
    Downloads: 0 This Week
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  • 4
    Neural Networks Collection

    Neural Networks Collection

    Neural Networks Collection

    This project implements in C++ a bunch of known Neural Networks. So far the project implements: LVQ in several variants, SOM in several variants, Hopfield network and Perceptron. Other neural network types are planned, but not implemented yet. The project can run in two modes: command line tool and Python 7.2 extension. Currently, Python version appears more functional, as it allows easy interaction with algorithms developed by other people.
    Downloads: 0 This Week
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