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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.

Comparing Machine Learning as a Service: Amazon, Microsoft Azure, Google Cloud AI KDnuggets   Subscribe to KDnuggets News  |       &

Computer brains need models that can be exposed to datasets, trained, tested and enriched over time as they learn what’s right, what’s wrong and ultimately what is altogether culturally and ethically appropriate or not.

A first-hand look from the .NET engineering teams

This leading textbook provides a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. This is the first machine learning textbook to include a comprehensive coverage of recent developments such as probabilistic graphical models and deterministic inference methods, and to emphasize a modern Bayesian perspective. It is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. This hard cover book has 738 pages in full colour, and there are 431 graded exercises (with solutions available below). Extensive support is provided for course instructors.

We’re excited to announce today the release of ML.NET 0.7 – the latest release of the cross-platform and open source machine learning framework for .NET developers (ML.NET 0.1 was released at //Build 2018). This release focuses on enabling better support for recommendation based ML tasks, enabling anomaly detection, enhancing the customizability of the machine learning pipelines, enabling using ML.NET in x86 apps, and more.

ML.NET is a free, cross-platform, and open source machine learning framework for .NET developers. It is also an extensible platform that powers Microsoft ser...