Showing 4 open source projects for "linux mini"

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  • 1
    PyTorch Geometric

    PyTorch Geometric

    Geometric deep learning extension library for PyTorch

    It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of an easy-to-use mini-batch loader for many small and single giant graphs, a large number of common benchmark datasets (based on simple interfaces to create your own), and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. We have outsourced a lot of...
    Downloads: 1 This Week
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  • 2
    PyG

    PyG

    Graph Neural Network Library for PyTorch

    PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support,...
    Downloads: 0 This Week
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  • 3
    Text-to-image Playground

    Text-to-image Playground

    A playground to generate images from any text prompt using SD

    dalle-playground is an open-source web application that allows users to generate images from natural language text prompts using modern text-to-image generative models. Originally built around DALL-E Mini, the project later transitioned to using Stable Diffusion, enabling more detailed and higher-quality image synthesis. The system combines a backend machine learning service with a browser-based frontend interface that lets users experiment interactively with prompt engineering and...
    Downloads: 0 This Week
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  • 4
    Learn_Data_Science_in_3_Months

    Learn_Data_Science_in_3_Months

    This is the Curriculum for "Learn Data Science in 3 Months"

    This project lays out a 12-week plan to go from basics to a portfolio-ready understanding of data science. It breaks the journey into clear stages: Python fundamentals, data wrangling, visualization, statistics, machine learning, and end-to-end projects. The schedule mixes learning and doing, encouraging you to build small deliverables each week—like notebooks, dashboards, and model demos—to reinforce skills. It also includes suggestions for datasets and problem domains so you aren’t stuck...
    Downloads: 0 This Week
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