AI Machine Learning Archives

Localize .NET applications with machine-translation

David Pine December 15, 2020 Dec 15, 2020 12/15/20

A GitHub Action harnessing Azure Cognitive Services Translator to automatically create translation files.

Introducing the Half type!

Prashanth Govindarajan August 31, 2020 Aug 31, 2020 08/31/20

The IEEE 754 specification defines many floating point types, including: binary16, binary32, binary64 and binary128. Most developers are familiar with binary32 (equivalent to float in C#) and binary64 (equivalent to double in C#). They provide a standard format to represent a wide range of values with a precision acceptable for many ...

February ML.NET Model Builder Updates

Bri Achtman March 2, 2020 Mar 2, 2020 03/2/20

With the ML.NET Model Builder, create custom machine learning models for scenarios like sentiment analysis, price prediction, and more without any machine learning experience and without leaving the .NET ecosystem!

Announcing ML.NET 1.4 general availability (Machine Learning for .NET)

Cesar De la Torre November 6, 2019 Nov 6, 2019 11/6/19

Coinciding with the Microsoft Ignite 2019 conference, we are thrilled to announce the GA release of ML.NET 1.4 and updates to Model Builder in Visual Studio, with exciting new machine learning features that will allow you to innovate your .NET applications. ML.NET is an open-source and cross-platform machine learning framework for .NET ...

ML.NET and Model Builder at .NET Conf 2019 (Machine Learning for .NET)

Cesar De la Torre September 24, 2019 Sep 24, 2019 09/24/19

We are excited today to announce updates to Model Builder and improvements in ML.NET. You can learn more in the “What’s new in ML.NET?.” session at .NET Conf. ML.NET is an open-source and cross-platform machine learning framework (Windows, Linux, macOS) for .NET developers. ML.NET offers Model Builder Model Builder (a simple UI tool) ...

Hey .NET! Have you tried ML.NET?

Bri Achtman August 21, 2019 Aug 21, 2019 08/21/19

ML.NET is an open source and cross-platform machine learning framework made for .NET developers. Using ML.NET you can easily build custom machine learning models for scenarios like sentiment analysis, price prediction, sales forecasting, recommendation, image classification, and more. ML.NET 1.0 was released at //Build 2019, and since then ...

Announcing ML.NET 1.2 and Model Builder updates (Machine Learning for .NET)

Cesar De la Torre July 17, 2019 Jul 17, 2019 07/17/19

We are excited to announce ML.NET 1.2 and updates to Model Builder and the CLI. ML.NET is an open-source and cross-platform machine learning framework for .NET developers. ML.NET also includes Model Builder (a simple UI tool for Visual Studio) and the ML.NET CLI (Command-line interface) to make it super easy to build custom Machine Learning (...

Submit to the Applied F# Challenge!

Phillip Carter February 5, 2019 Feb 5, 2019 02/5/19

This post was written by Lena Hall, a Senior Cloud Developer Advocate at Microsoft. F# Software Foundation has recently announced their new initiative — Applied F# Challenge! We encourage you to participate and send your submissions about F# on Azure through the participation form. Applied F# Challenge is a new initiative to ...

Tell us your thoughts on ML.NET, an open source and cross-platform machine learning framework

Bri Achtman January 9, 2019 Jan 9, 2019 01/9/19

ML.NET is an open source and cross-platform machine learning framework made for .NET developers. .NET developers can use their C# or F# skills to easily integrate custom machine learning into their web, mobile, desktop, gaming, or IoT applications without any prior expertise in developing or tuning machine learning models. ML.NET covers many ...

Announcing ML.NET 0.8 – Machine Learning for .NET

Cesar De la Torre December 4, 2018 Dec 4, 2018 12/4/18

ML.NET is an open-source and cross-platform framework (Windows, Linux, macOS) which makes machine learning accessible for .NET developers. ML.NET allows you to create and use machine learning models targeting scenarios to achieve common tasks such as sentiment analysis, issue classification, forecasting, recommendations, fraud detection...

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