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

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

    List of GitHub repositories of the project: IBM This data is not pre-processed List of GitHub repositories of the project: IBM Cloud This data is not pre-processed List of GitHub repositories of the project: Build Lab Team This data is not pre-processed List of GitHub repositories of the project: Terraform IBM Modules This data is not pre-processed

  3. List of datasets in computer vision and image processing

    en.wikipedia.org/wiki/List_of_datasets_in...

    Some publicly available fonts and extracted glyphs from them to make a dataset similar to MNIST. There are 10 classes, with letters A–J taken from different fonts. Classes labelled, training set splits created. 500,000 Images Classification 2011 [39] Yaroslav Bulatov Linnaeus 5 dataset Images of 5 classes of objects.

  4. pandas (software) - Wikipedia

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

    Pandas (styled as pandas) is a software library written for the Python programming language for data manipulation and analysis.In particular, it offers data structures and operations for manipulating numerical tables and time series.

  5. MNIST database - Wikipedia

    en.wikipedia.org/wiki/MNIST_database

    Extended MNIST (EMNIST) is a newer dataset developed and released by NIST to be the (final) successor to MNIST. [ 15 ] [ 16 ] MNIST included images only of handwritten digits. EMNIST includes all the images from NIST Special Database 19 (SD 19), which is a large database of 814,255 handwritten uppercase and lower case letters and digits.

  6. GitHub - Wikipedia

    en.wikipedia.org/wiki/Github

    GitHub (/ ˈ ɡ ɪ t h ʌ b /) is a proprietary developer platform that allows developers to create, store, manage, and share their code. It uses Git to provide distributed version control and GitHub itself provides access control, bug tracking, software feature requests, task management, continuous integration, and wikis for every project. [8]

  7. Retrieval-augmented generation - Wikipedia

    en.wikipedia.org/wiki/Retrieval-augmented_generation

    Retrieval-Augmented Generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to augment information drawn from its own vast, static training data.

  8. Extract, transform, load - Wikipedia

    en.wikipedia.org/wiki/Extract,_transform,_load

    An example would be an expense and cost recovery system such as used by accountants, consultants, and law firms. The data usually ends up in the time and billing system, although some businesses may also utilize the raw data for employee productivity reports to Human Resources (personnel dept.) or equipment usage reports to Facilities Management.

  9. Amazon SageMaker - Wikipedia

    en.wikipedia.org/wiki/Amazon_SageMaker

    While the web API is agnostic to the programming language used by the developer, Amazon provides SageMaker API bindings for a number of languages, including Python, JavaScript, Ruby, Java, and Go. [13] [14] In addition, SageMaker provides managed Jupyter Notebook instances for interactively programming SageMaker and other applications. [15] [16]