Showing 7 open source projects for "collection"

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  • 1
    Stanza

    Stanza

    Stanford NLP Python library for many human languages

    Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Stanza is a Python natural language analysis package. It contains tools, which can be used in a pipeline, to convert a string containing human language text into lists of sentences and words, to generate base forms of those words, their parts of speech and morphological features, to give a syntactic structure dependency parse, and to recognize named entities. ...
    Downloads: 3 This Week
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  • 2
    Jraph

    Jraph

    A Graph Neural Network Library in Jax

    ...The library includes a comprehensive set of utilities for batching, padding, masking, and partitioning graph data, making it ideal for distributed and large-scale GNN experiments. Jraph also comes with a model zoo—a collection of forkable reference implementations of common message-passing GNN architectures, such as Graph Networks, Graph Convolutional Networks, and Graph Attention Networks.
    Downloads: 0 This Week
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  • 3
    Lucid

    Lucid

    A collection of infrastructure and tools for research

    Lucid is a collection of infrastructure and tools for research in neural network interpretability. Lucid is research code, not production code. We provide no guarantee it will work for your use case. Lucid is maintained by volunteers who are unable to provide significant technical support. Start visualizing neural networks with no setup. The following notebooks run right from your browser, thanks to Collaboratory.
    Downloads: 0 This Week
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  • 4
    The Neural Process Family

    The Neural Process Family

    This repository contains notebook implementations

    Neural Processes (NPs) is a collection of interactive Jupyter/Colab notebook implementations developed by Google DeepMind, showcasing three foundational probabilistic machine learning models: Conditional Neural Processes (CNPs), Neural Processes (NPs), and Attentive Neural Processes (ANPs). These models combine the strengths of neural networks and stochastic processes, allowing for flexible function approximation with uncertainty estimation.
    Downloads: 0 This Week
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    Swift AI

    Swift AI

    The Swift machine learning library

    Swift AI is a high-performance deep learning library written entirely in Swift. We currently offer support for all Apple platforms, with Linux support coming soon. Swift AI includes a collection of common tools used for artificial intelligence and scientific applications. A flexible, fully-connected neural network with support for deep learning. Optimized specifically for Apple hardware, using advanced parallel processing techniques. We've created some example projects to demonstrate the usage of Swift AI. Each resides in their own repository and can be built with little or no configuration. ...
    Downloads: 0 This Week
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  • 6
    nunn

    nunn

    This is an implementation of a machine learning library in C++17

    nunn is a collection of ML algorithms and related examples written in modern C++17.
    Downloads: 0 This Week
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  • 7
    TIKAPP is becoming a collection of tools for simulation of neural networks. The first available part is an ANSI-C++ library with support for backpropation networks.
    Downloads: 0 This Week
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