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  2. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    Dataset HF card, and project's GitHub repository. [393] Diggelmann et al. Climate News dataset A dataset for NLP and climate change media researchers The dataset is made up of a number of data artifacts (JSON, JSONL & CSV text files & SQLite database) Climate news DB, Project's GitHub repository [394] ADGEfficiency Climatext

  3. Orange (software) - Wikipedia

    en.wikipedia.org/wiki/Orange_(software)

    Orange is an open-source software package released under GPL and hosted on GitHub.Versions up to 3.0 include core components in C++ with wrappers in Python.From version 3.0 onwards, Orange uses common Python open-source libraries for scientific computing, such as numpy, scipy and scikit-learn, while its graphical user interface operates within the cross-platform Qt framework.

  4. Insight Segmentation and Registration Toolkit - Wikipedia

    en.wikipedia.org/wiki/Insight_Segmentation_and...

    The Insight/Examples/ source code examples distributed with ITK. The source code is available. In addition, it is heavily commented and works in combination with the ITK Software Guide. The separate InsightApplications checkout. The Applications web pages. These are extensive descriptions, with images and references, of the examples found in #1 ...

  5. List of in-memory databases - Wikipedia

    en.wikipedia.org/wiki/List_of_in-memory_databases

    C, C++, Python, Lua, C#, etc. [10] Redis Source Available License v2 and the Server Side Public License v1 [11] Redis is a source-available software project that implements data structure servers. It is networked, in-memory, and stores keys with optional durability. SafePeak: SafePeak Technologies Proprietary

  6. CatBoost - Wikipedia

    en.wikipedia.org/wiki/Catboost

    It works on Linux, Windows, macOS, and is available in Python, [8] R, [9] and models built using CatBoost can be used for predictions in C++, Java, [10] C#, Rust, Core ML, ONNX, and PMML. The source code is licensed under Apache License and available on GitHub. [6] InfoWorld magazine awarded the library "The best machine learning tools" in 2017.

  7. Graph-tool - Wikipedia

    en.wikipedia.org/wiki/Graph-tool

    graph-tool is a Python module for manipulation and statistical analysis of graphs (AKA networks).The core data structures and algorithms of graph-tool are implemented in C++, making extensive use of metaprogramming, based heavily on the Boost Graph Library. [1]

  8. Torch (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Torch_(machine_learning)

    [13] [better source needed] It has been used to build hardware implementations for data flows like those found in neural networks. [14] Facebook has released a set of extension modules as open source software. [15]

  9. Project Jupyter - Wikipedia

    en.wikipedia.org/wiki/Project_Jupyter

    The main parts of the Jupyter Notebooks are: Metadata, Notebook format and list of cells. Metadata is a data Dictionary of definitions to set up and display the notebook. Notebook Format is a version number of the software. List of cells are different types of Cells for Markdown (display), Code (to execute), and output of the code type cells. [23]