Showing 18 open source projects for "use case"

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  • 1
    Flower

    Flower

    Flower: A Friendly Federated Learning Framework

    A unified approach to federated learning, analytics, and evaluation. Federate any workload, any ML framework, and any programming language. Federated learning systems vary wildly from one use case to another. Flower allows for a wide range of different configurations depending on the needs of each individual use case. Flower originated from a research project at the University of Oxford, so it was built with AI research in mind. Many components can be extended and overridden to build new state-of-the-art systems. Different machine learning frameworks have different strengths. ...
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  • 2
    UpTrain

    UpTrain

    Your open-source LLM evaluation toolkit

    ...Unleash unparalleled power with a single line of code and tailor every detail as per as your use-case.
    Downloads: 0 This Week
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  • 3
    TorchServe

    TorchServe

    Serve, optimize and scale PyTorch models in production

    ...Expressive handlers: An expressive handler architecture that makes it trivial to support inferencing for your use case with many supported out of the box. Out-of-box support for system-level metrics with Prometheus exports, custom metrics and PyTorch profiler support.
    Downloads: 0 This Week
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  • 4
    Shapash

    Shapash

    Explainability and Interpretability to Develop Reliable ML models

    Shapash is a Python library dedicated to the interpretability of Data Science models. It provides several types of visualization that display explicit labels that everyone can understand. Data Scientists can more easily understand their models, share their results and easily document their projects in an HTML report. End users can understand the suggestion proposed by a model using a summary of the most influential criteria.
    Downloads: 0 This Week
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  • 5
    Fairlearn

    Fairlearn

    A Python package to assess and improve fairness of ML models

    ...In Fairlearn, we define whether an AI system is behaving unfairly in terms of its impact on people – i.e., in terms of harm. Fairness of AI systems is about more than simply running lines of code. In each use case, both societal and technical aspects shape who might be harmed by AI systems and how. There are many complex sources of unfairness and a variety of societal and technical processes for mitigation, not just the mitigation algorithms in our library.
    Downloads: 1 This Week
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  • 6
    DeepPavlov

    DeepPavlov

    A library for deep learning end-to-end dialog systems and chatbots

    DeepPavlov makes it easy for beginners and experts to create dialogue systems. The best place to start is with user-friendly tutorials. They provide quick and convenient introduction on how to use DeepPavlov with complete, end-to-end examples. No installation needed. Guides explain the concepts and components of DeepPavlov. Follow step-by-step instructions to install, configure and extend DeepPavlov framework for your use case. DeepPavlov is an open-source framework for chatbots and virtual assistants development. It has comprehensive and flexible tools that let developers and NLP researchers create production-ready conversational skills and complex multi-skill conversational assistants. ...
    Downloads: 0 This Week
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  • 7
    AutoGluon

    AutoGluon

    AutoGluon: AutoML for Image, Text, and Tabular Data

    ...Leverage automatic hyperparameter tuning, model selection/ensembling, architecture search, and data processing. Easily improve/tune your bespoke models and data pipelines, or customize AutoGluon for your use-case. AutoGluon is modularized into sub-modules specialized for tabular, text, or image data. You can reduce the number of dependencies required by solely installing a specific sub-module via: python3 -m pip install <submodule>.
    Downloads: 0 This Week
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  • 8
    PyTorch Geometric Temporal

    PyTorch Geometric Temporal

    Spatiotemporal Signal Processing with Neural Machine Learning Models

    The library consists of various dynamic and temporal geometric deep learning, embedding, and Spatio-temporal regression methods from a variety of published research papers. Moreover, it comes with an easy-to-use dataset loader, train-test splitter and temporal snaphot iterator for dynamic and temporal graphs. The framework naturally provides GPU support. It also comes with a number of benchmark datasets from the epidemiological forecasting, sharing economy, energy production and web traffic...
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  • 9
    AIF360

    AIF360

    A comprehensive set of fairness metrics for datasets

    ...The complete API is also available. Being a comprehensive set of capabilities, it may be confusing to figure out which metrics and algorithms are most appropriate for a given use case. To help, we have created some guidance material that can be consulted.
    Downloads: 1 This Week
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  • 10
    AI Explainability 360

    AI Explainability 360

    Interpretability and explainability of data and machine learning model

    ...It may therefore be confusing to figure out which algorithms are most appropriate for a given use case.
    Downloads: 0 This Week
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  • 11
    pyntcloud

    pyntcloud

    pyntcloud is a Python library for working with 3D point clouds

    This page will introduce the general concept of point clouds and illustrate the capabilities of pyntcloud as a point cloud processing tool. Point clouds are one of the most relevant entities for representing three dimensional data these days, along with polygonal meshes (which are just a special case of point clouds with connectivity graph attached). In its simplest form, a point cloud is a set of points in a cartesian coordinate system. Accurate 3D point clouds can nowadays be (easily and cheaply) acquired from different sources. pyntcloud enables simple and interactive exploration of point cloud data, regardless of which sensor was used to generate it or what the use case is. ...
    Downloads: 0 This Week
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  • 12
    AI Platform Training and Prediction
    AI Platform Training and Prediction is a collection of machine learning example projects that demonstrate how to train, deploy, and serve models using Google Cloud AI Platform and related services. It includes a wide variety of implementations across frameworks such as TensorFlow, PyTorch, scikit-learn, and XGBoost, allowing developers to explore different approaches to building ML solutions. The repository covers the full machine learning lifecycle, including data preprocessing, model...
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  • 13
    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. It's a Jupyter notebook environment that requires no setup to use and runs entirely in the cloud. You can run the notebooks on your local machine, too. ...
    Downloads: 0 This Week
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  • 14
    xLearn

    xLearn

    High performance, easy-to-use, and scalable machine learning (ML)

    ...In that case, if you are a user of liblinear, libfm, and libffm, now xLearn is another better choice.
    Downloads: 0 This Week
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  • 15
    Caffe2

    Caffe2

    Caffe2 is a lightweight, modular, and scalable deep learning framework

    ...Modularity and being designed for both scale and mobile deployments are the high-level answers to the first question. In many ways Caffe2 is an un-framework because it is so flexible and modular. The original Caffe framework was useful for large-scale product use cases, especially with its unparalleled performance and well tested C++ codebase. Caffe has some design choices that are inherited from its original use case: conventional CNN applications.
    Downloads: 0 This Week
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  • 16

    GA-EoC

    GeneticAlgorithm-based search for Heterogeneous Ensemble Combinations

    In data classification, there are no particular classifiers that perform consistently in every case. This is even worst in case of both the high dimensional and class-imbalanced datasets. To overcome the limitations of class-imbalanced data, we split the dataset using a random sub-sampling to balance them. Then, we apply the (alpha,beta)-k feature set method to select a better subset of features and combine their outputs to get a consolidated feature set for classifier training. To...
    Downloads: 0 This Week
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  • 17
    Neurology Diagnosis System
    “Neurology Diagnosis System” is a web-based expert system for diagnosis of neurologic disorders or the disorders of our nervous system. Health assistants in remote areas can use the system to diagnose neurologic patients in the absence of neurolo
    Downloads: 0 This Week
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  • 18

    PyCBR

    This project aims to provide a simple python interface for CBR

    ...This script is still in its infant stage of CBR & script matching. Nontheless you could use it to do many crazy stuff. Enjoy!!!
    Downloads: 0 This Week
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