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  2. Comparison of deep learning software - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_deep...

    Windows, macOS, Linux, Cloud computing: C++, Wolfram Language, CUDA: Wolfram Language: Yes No Yes No Yes Yes [75] Yes Yes Yes Yes [76] Yes Software Creator Initial release Software license [a] Open source Platform Written in Interface OpenMP support OpenCL support CUDA support ROCm support [77] Automatic differentiation [2] Has pretrained ...

  3. Amazon SageMaker - Wikipedia

    en.wikipedia.org/wiki/Amazon_SageMaker

    Amazon SageMaker AI is a cloud-based machine-learning platform that allows the creation, training, and deployment by developers of machine-learning (ML) models on the cloud. [1] It can be used to deploy ML models on embedded systems and edge-devices. [2] [3] The platform was launched in November 2017. [4]

  4. List of artificial intelligence projects - Wikipedia

    en.wikipedia.org/wiki/List_of_artificial...

    AlphaFold is a deep learning based system developed by DeepMind for prediction of protein structure. [76] Otter.ai is a speech-to-text synthesis and summary platform, which allows users to record online meetings as text. It additionally creates live captions during meetings.

  5. Cloud-computing comparison - Wikipedia

    en.wikipedia.org/wiki/Cloud-computing_comparison

    Oracle Cloud Platform: 2014 PythonAnywhere: 2012 Python: Salesforce App Cloud: Providers on IaaS.

  6. Google Cloud Platform - Wikipedia

    en.wikipedia.org/wiki/Google_Cloud_Platform

    Google Cloud Platform is a part [7] of Google Cloud, which includes the Google Cloud Platform public cloud infrastructure, as well as Google Workspace (G Suite), enterprise versions of Android and ChromeOS, and application programming interfaces (APIs) for machine learning and enterprise mapping services.

  7. Cloud computing - Wikipedia

    en.wikipedia.org/wiki/Cloud_computing

    Cloud platforms also offer managed services and tools, such as artificial intelligence, data analytics, and machine learning, which might otherwise require significant in-house expertise and infrastructure investment. [11] [12] [13]

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