3 projects for "static code analysis" with 2 filters applied:

  • Go from Data Warehouse to Data and AI platform with BigQuery Icon
    Go from Data Warehouse to Data and AI platform with BigQuery

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    MongoDB Atlas runs apps anywhere

    Deploy in 115+ regions with the modern database for every enterprise.

    MongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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  • 1
    mlforecast

    mlforecast

    Scalable machine learning for time series forecasting

    mlforecast is a time-series forecasting framework built around machine-learning models, designed to make forecasting both efficient and scalable. It lets you apply any regressor that follows the typical scikit-learn API, for example, gradient-boosted trees or linear models, to time-series data by automating much of the messy feature engineering and data preparation. Instead of writing custom code to build lagged features, rolling statistics, and date-based predictors, mlforecast generates...
    Downloads: 0 This Week
    Last Update:
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  • 2
    d2l-zh

    d2l-zh

    Chinese-language edition of Dive into Deep Learning

    d2l‑zh is the Chinese-language edition of Dive into Deep Learning, an interactive, open‑source deep learning textbook that combines code, math, and explanatory text. It features runnable Jupyter notebooks compatible with multiple frameworks (e.g., PyTorch, MXNet, TensorFlow), comprehensive theoretical analysis, and exercises. Widely adopted in over 70 countries and used by more than 500 universities for teaching deep learning.
    Downloads: 0 This Week
    Last Update:
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  • 3
    Spark Python Notebooks

    Spark Python Notebooks

    Apache Spark & Python (pySpark) tutorials for Big Data Analysis

    Spark Python Notebooks is a curated collection of example Jupyter notebooks designed to help developers and data engineers learn Apache Spark using Python in an interactive environment. Rather than only providing static code files, this project uses notebooks to teach practical data processing workflows, exposing users to real Spark programming patterns like working with RDDs, DataFrames, and distributed computations. These notebooks often demonstrate how to transform, analyze, and visualize large datasets using PySpark APIs, which mirrors many real-world big data use cases. ...
    Downloads: 6 This Week
    Last Update:
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