Showing 3 open source projects for "compare"

View related business solutions
  • MongoDB Atlas runs apps anywhere Icon
    MongoDB Atlas runs apps anywhere

    Deploy in 115+ regions with the modern database for every enterprise.

    MongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
    Start Free
  • Train ML Models With SQL You Already Know Icon
    Train ML Models With SQL You Already Know

    BigQuery automates data prep, analysis, and predictions with built-in AI assistance.

    Build and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
    Try Free
  • 1
    bufferline.nvim

    bufferline.nvim

    A snazzy bufferline for Neovim

    A snazzy buffer line (with tab page integration) for Neovim built using Lua. This plugin shamelessly attempts to emulate the aesthetics of GUI text editors/Doom Emacs. It is advised that you specify either the latest tag or a specific tag and bump them manually if you'd prefer to inspect changes before updating.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 2

    BallPlay Cupid

    Arcade puzzle game with balls

    BallPlay Cupid is an arcade puzzle games, in which you have a task to fulfil by changing the direction of the balls, by using tools. Complete the task and keep the required number of balls away from destruction.
    Downloads: 2 This Week
    Last Update:
    See Project
  • 3
    char-rnn

    char-rnn

    Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN)

    char-rnn is a classic codebase for training multi-layer recurrent neural networks on raw text to build character-level language models that learn to predict the next character in a sequence. It supports common recurrent architectures including vanilla RNNs as well as LSTM and GRU variants, letting users compare behavior and output quality across model types. It is straightforward: you provide a single text file, train the model to minimize next-character prediction loss, then sample from the trained network to generate new text one character at a time in the style of the dataset. The project is designed for experimentation, offering tunable settings for depth, hidden size, dropout, sequence length, and sampling temperature to control creativity and coherence. ...
    Downloads: 0 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next