Showing 4 open source projects for "practice test"

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    TorchCode

    TorchCode

    Practice implementing softmax, attention, GPT-2 and more

    ...It runs in a Jupyter-based environment, allowing users to write, test, and debug their code interactively while receiving immediate feedback. An automated judging system evaluates correctness, gradient flow, and numerical stability, helping users understand both functional and theoretical aspects of their implementations.
    Downloads: 0 This Week
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  • 2
    AIF360

    AIF360

    A comprehensive set of fairness metrics for datasets

    This extensible open source toolkit can help you examine, report, and mitigate discrimination and bias in machine learning models throughout the AI application lifecycle. We invite you to use and improve it. The AI Fairness 360 toolkit is an extensible open-source library containing techniques developed by the research community to help detect and mitigate bias in machine learning models throughout the AI application lifecycle. AI Fairness 360 package is available in both Python and R. The...
    Downloads: 0 This Week
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  • 3
    Machine Learning Homework

    Machine Learning Homework

    Matlab Coding homework for Machine Learning

    ...It includes implementations of standard machine learning algorithms (such as regression, classification, etc.), scripts for data loading and preprocessing, and evaluation routines (e.g. accuracy, error metrics). Because it is structured as homework or practice material, the code is likely intended more for didactic use than for production deployment. It may contain comments, example datasets, and perhaps test scripts. The repository does not seem to be heavily maintained as a software project; rather, it functions as a library of solved problems and educational examples. The project is useful if you want working MATLAB examples of classic ML techniques, to study, adapt, or compare with your own implementations.
    Downloads: 0 This Week
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  • 4
    Coursera Machine Learning

    Coursera Machine Learning

    Coursera Machine Learning By Prof. Andrew Ng

    CourseraMachineLearning is a personal collection of resources, notes, and programming exercises from Andrew Ng’s popular Machine Learning course on Coursera. It consolidates lecture references, programming tutorials, test cases, and supporting materials into one repository for easier review and practice. The project highlights fundamental machine learning concepts such as hypothesis functions, cost functions, gradient descent, bias-variance tradeoffs, and regression models. It also organizes week-by-week course schedules with links to exercises, lecture notes, and additional resources. ...
    Downloads: 22 This Week
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