Showing 5 open source projects for "include"

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  • 1
    DALI

    DALI

    A GPU-accelerated library containing highly optimized building blocks

    ...It can be used as a portable drop-in replacement for built-in data loaders and data iterators in popular deep learning frameworks. Deep learning applications require complex, multi-stage data processing pipelines that include loading, decoding, cropping, resizing, and many other augmentations. These data processing pipelines, which are currently executed on the CPU, have become a bottleneck, limiting the performance and scalability of training and inference. DALI addresses the problem of the CPU bottleneck by offloading data preprocessing to the GPU. ...
    Downloads: 1 This Week
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  • 2
    ML++

    ML++

    A library created to revitalize C++ as a machine learning front end

    ...This is especially important in the world of ML, as new algorithms and techniques are being developed day by day. Here are a couple of things currently being developed for ML++. Call the optimizer that you would like to use. For iterative optimizers such as gradient descent, include the learning rate, epoch number, and whether or not to utilize the UI panel.
    Downloads: 0 This Week
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  • 3
    This project aims to develop and share fast frequent subgraph mining and graph learning algorithms. Currently we release the frequent subgraph mining package FFSM and later we will include new functions for graph regression and classification package
    Downloads: 0 This Week
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  • 4
    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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    AI-generated apps that pass security review

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  • 5
    Adaptive Gaussian Filtering

    Adaptive Gaussian Filtering

    Machine learning with Gaussian kernels.

    Libagf is a machine learning library that includes adaptive kernel density estimators using Gaussian kernels and k-nearest neighbours. Operations include statistical classification, interpolation/non-linear regression and pdf estimation. For statistical classification there is a borders training feature for creating fast and general pre-trained models that nonetheless return the conditional probabilities. Libagf also includes clustering algorithms as well as comparison and validation routines. ...
    Downloads: 0 This Week
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