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  2. Elements of AI - Wikipedia

    en.wikipedia.org/wiki/Elements_of_AI

    Elements of AI is a massive open online course (MOOC) teaching the basics of artificial intelligence. [1] The course, originally launched in 2018, is designed and organized by the University of Helsinki and learning technology company MinnaLearn. [2]

  3. List of programming languages for artificial intelligence

    en.wikipedia.org/wiki/List_of_programming...

    It is mostly used for numerical analysis, computational science, and machine learning. [6] C# can be used to develop high level machine learning models using Microsoft’s .NET suite. ML.NET was developed to aid integration with existing .NET projects, simplifying the process for existing software using the .NET platform.

  4. Artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Artificial_intelligence

    Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems.It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. [1]

  5. Investing in artificial intelligence (AI): A beginner’s guide

    www.aol.com/finance/investing-artificial...

    How to invest in artificial intelligence For most retail investors, there’s a chance you already have exposure to AI, as many large U.S. public companies are either using AI or are actively ...

  6. Deep learning - Wikipedia

    en.wikipedia.org/wiki/Deep_learning

    Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning.The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.

  7. Machine learning - Wikipedia

    en.wikipedia.org/wiki/Machine_learning

    Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices.

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