Showing 9 open source projects for "artificial neural network code"

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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...
    Downloads: 4 This Week
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  • 2
    Frevo

    Frevo

    Frevo is probably the simplest tool for evolutionary design

    FREVO is an open-source framework developed in Java to help engineers and scientists in evolutionary design or optimization tasks. The major feature of FREVO is the componentwise decomposition and separation of the key building blocks for each optimization tasks. We identify these as the problem definition, solution representation and the optimization method. This structure enables the components to be designed separately allowing the user to easily swap and evaluate different configurations...
    Downloads: 0 This Week
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  • 3
    Brain Tokyo Workshop

    Brain Tokyo Workshop

    Experiments and code from Google Brain’s Tokyo research workshop

    The Brain Tokyo Workshop repository hosts a collection of research materials and experimental code developed by the Google Brain team based in Tokyo. It showcases a variety of cutting-edge projects in artificial intelligence, particularly in the areas of neuroevolution, reinforcement learning, and model interpretability. Each project explores innovative approaches to learning, prediction, and creativity in neural networks, often through unconventional or biologically inspired methods. ...
    Downloads: 0 This Week
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  • 4
    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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  • 5
    NiftyNet

    NiftyNet

    An open-source convolutional neural networks platform for research

    An open-source convolutional neural networks platform for medical image analysis and image-guided therapy. NiftyNet is a TensorFlow-based open-source convolutional neural networks (CNNs) platform for research in medical image analysis and image-guided therapy. NiftyNet’s modular structure is designed for sharing networks and pre-trained models. Using this modular structure you can get started with established pre-trained networks using built-in tools. Adapt existing networks to your imaging...
    Downloads: 1 This Week
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  • 6
    DaNNet

    DaNNet

    Deep Artificial Neural Network framework using Armadillo

    DaNNet is a C++ deep neural network library using the Armadillo library as a base. It is intended to be a small and easy to use framework with no other dependencies than Armadillo. It uses independent layer-wise optimization giving you full flexibility to train your network.
    Downloads: 0 This Week
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  • 7

    TML - Text Mining Library for LSA & CMM

    TML is a Java Library for LSA and extracting Concept Maps from text

    TML has moved to http://www.villalon.cl/tml.html and the code to https://github.com/villalon/tml
    Downloads: 0 This Week
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  • 8
    NeuronDotNet is a neural network engine written in C#. It provides an interface for advanced AI programmers to design various types of artificial neural networks and use them.
    Downloads: 0 This Week
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  • 9
    This project uses massively parallel Graphics Processing Units(GPU) for neural network(Backpropagation) purposes.
    Downloads: 0 This Week
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