Showing 9 open source projects for "dynamicreports-examples"

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    XGBoost

    XGBoost

    Scalable and Flexible Gradient Boosting

    ...XGBoost can be used for Python, Java, Scala, R, C++ and more. It can run on a single machine, Hadoop, Spark, Dask, Flink and most other distributed environments, and is capable of solving problems beyond billions of examples.
    Downloads: 9 This Week
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    the-turing-way

    the-turing-way

    Book repository for The Turing Way

    A community‑led open handbook and living documentation project from the Alan Turing Institute, providing best practices and open guidance for reproducible, ethical, collaborative data science and research.
    Downloads: 1 This Week
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  • 3
    cuDF

    cuDF

    GPU DataFrame Library

    Built based on the Apache Arrow columnar memory format, cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data. cuDF provides a pandas-like API that will be familiar to data engineers & data scientists, so they can use it to easily accelerate their workflows without going into the details of CUDA programming. For additional examples, browse our complete API documentation, or check out our more detailed notebooks. cuDF can be installed with conda (miniconda, or the full Anaconda distribution) from the rapidsai channel. cuDF is supported only on Linux, and with Python versions 3.7 and later. The RAPIDS suite of open-source software libraries aims to enable the execution of end-to-end data science and analytics pipelines entirely on GPUs. ...
    Downloads: 3 This Week
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  • 4
    Recommenders

    Recommenders

    Best practices on recommendation systems

    The Recommenders repository provides examples and best practices for building recommendation systems, provided as Jupyter notebooks. The module reco_utils contains functions to simplify common tasks used when developing and evaluating recommender systems. Several utilities are provided in reco_utils to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting training/test data.
    Downloads: 0 This Week
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  • 5
    Self-learning-Computer-Science

    Self-learning-Computer-Science

    Resources to learn computer science in your spare time

    ...It’s aimed at learners who find traditional course structures restrictive and want a flexible, self-paced path through CS, with a focus on building depth and breadth rather than shortcut exam skills. The repository provides a roadmap, references, teaching materials, and sometimes the author’s own project examples, offering both guidance and community support. Because the CS field is broad, the structure helps learners allocate study time, avoid duplication, and benefit from “best in class” resources instead of randomly browsing.
    Downloads: 0 This Week
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  • 6
    Data Science Notes

    Data Science Notes

    Curated collection of data science learning materials

    ...It spans foundational math and statistics through data wrangling, visualization, machine learning, and practical project organization. The content emphasizes hands-on understanding by pairing narrative notes with runnable examples, making it useful for both self-study and classroom settings. Because it aggregates topics in one place, learners can move linearly or jump into specific areas as needed during projects. The notes also highlight common pitfalls and good practices, which helps beginners adopt professional habits early. It’s a living resource that many students consult when revising fundamentals or exploring adjacent tools in the ecosystem.
    Downloads: 0 This Week
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  • 7
    Amazon SageMaker Examples

    Amazon SageMaker Examples

    Jupyter notebooks that demonstrate how to build models using SageMaker

    Welcome to Amazon SageMaker. This projects highlights example Jupyter notebooks for a variety of machine learning use cases that you can run in SageMaker. If you’re new to SageMaker we recommend starting with more feature-rich SageMaker Studio. It uses the familiar JupyterLab interface and has seamless integration with a variety of deep learning and data science environments and scalable compute resources for training, inference, and other ML operations. Studio offers teams and companies...
    Downloads: 0 This Week
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  • 8
    Forecasting Best Practices

    Forecasting Best Practices

    Time Series Forecasting Best Practices & Examples

    ...The examples and best practices are provided as Python Jupyter notebooks and R markdown files and a library of utility functions.
    Downloads: 0 This Week
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  • 9
    Data Science Specialization

    Data Science Specialization

    Course materials for the Data Science Specialization on Coursera

    ...It contains the source code and resources used throughout the specialization’s courses, covering a broad range of data science concepts and techniques. The repository is designed as a shared space for code examples, datasets, and instructional materials, helping learners follow along with lectures and assignments. It spans essential topics such as R programming, data cleaning, exploratory data analysis, statistical inference, regression models, machine learning, and practical data science projects. By providing centralized resources, the repo makes it easier for students to practice concepts and replicate examples from the curriculum. ...
    Downloads: 5 This Week
    Last Update:
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