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ML.NET makes machine learning accessible to the .NET developer community. Up until now, the machine learning space has been dominated by other languages such as Python and C++. With ML.NET, .NET developers get access to a whole host of machine learning techniques for solving various problems. ML.NET makes integrating intelligent systems into an existing codebase …

ML.NET is an open-source and cross-platform machine learning framework (Windows, Linux, macOS) for .NET developers. Using ML.NET, developers can leverage their existing tools and skillsets to develop and infuse custom AI into their applications by creating custom machine learning models for common scenarios like Sentiment Analysis,

For the last month I’ve been carrying around a small square micro-controller board in my laptop bag, and I’ve been dying to tell people more about it. But I haven’t been able to until today. Announced a few minutes ago from the stage of this year’s TensorFlow Dev Summit in Santa Clara, CA, by Pete Warden, part of the TensorFlow Lite team at Google, say “Hello” to the SparkFun Edge.

ML.NET is an open-source and cross-platform machine learning framework (Windows, Linux, macOS) for .NET developers. Using ML.NET, developers can leverage their existing tools and skillsets to develop and infuse custom AI into their applications by creating custom machine learning models for common scenarios like Sentiment Analysis,

ML.NET is an open-source and cross-platform machine learning framework (Windows, Linux, macOS) for .NET developers. Using ML.NET, developers can leverage their existing tools and skillsets to develop and infuse custom AI into their applications by creating custom machine learning models. ML.NET allows you to create and use machine learning models targeting common tasks such as classification, regression, clustering, ranking, recommendations and anomaly detection. It also supports the broader open source ecosystem by proving integration with popular deep-learning frameworks like TensorFlow and interoperability through ONNX. Some common use cases of ML.NET are scenarios like Sentiment Analysis, Recommendations, Image Classification, Sales Forecast, etc. Please see our samples for more scenarios.

FsLab is a curated collection of open source F# packages for data-science. Together with your editor or Jupyter notebook these packages allow you to rapidly develop scalable, high-performance analytics and visualizations using succinct, type-safe, production-ready code. Download

Online code repository GitHub has pulled together the 10 most popular programming languages used for machine learning hosted on its service, and, while Python tops the list, there's a few surprises.

ML.NET is an open-source and cross-platform machine learning framework (Windows, Linux, macOS) for .NET developers. Using ML.NET, developers can leverage their existing tools and skillsets to develop and infuse custom AI into their applications by creating custom machine learning models.

Machine Learning in NET (ML NET) full tutorial

The Quantum Architecture and Computation group launched Q#, our quantum computing programming language, a year ago on December 11th, 2017. Q# 0.1 was the result of a lot of hard work from a small, dedicated team of developers, researchers, and program managers. We had made the decision to build a domain-specific language for quantum computing about six months before we launched, so we were on a very tight schedule. We were lucky to have a great team of people who all pitched in and did what needed to be done so that we could meet our extremely aggressive timetable.