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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. Smalltalk has been used extensively for simulations, neural networks, machine learning, and genetic algorithms.
Adobe Express, formerly Adobe Spark and later Creative Cloud Express, is a content creation tool developed by Adobe. [1] [2] [3] It is a cloud-based design platform where users can create videos, PDF documents, web pages, graphics and other digital assets.
Adobe Systems's Graphic Design software and image editor. Bluebeam Revu: Proprietary: A commercial PDF editor, markup and collaboration product aimed at engineering and architectural markets. Foxit Reader: Freeware: Highlight text, draw lines, measure distances of PDF documents. Foxit PDF Editor Suite: Free trial: Integrated PDF Editing and ...
Generative artificial intelligence (generative AI, GenAI, [1] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. [ 2 ] [ 3 ] [ 4 ] These models learn the underlying patterns and structures of their training data and use them to produce new data [ 5 ] [ 6 ] based on ...
GitHub Copilot is a code completion and automatic programming tool developed by GitHub and OpenAI that assists users of Visual Studio Code, Visual Studio, Neovim, and JetBrains integrated development environments (IDEs) by autocompleting code. [1]
Microsoft also added extensions to C++, called Managed Extensions for C++, so .NET programs could be created in C++. Visual Studio .NET can produce applications targeting Windows (using the Windows Forms part of the .NET Framework), the Web (using ASP.NET and Web Services) and, with an add-in, portable devices (using the .NET Compact Framework).
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
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.
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