ML.NET 是一个面向 .NET 开发人员的开源和跨平台机器学习框架，它包括 Model Builder 和 CLI(命令行接口)，让使用自动机器学习(AutoML)构建自定义机器学习模型变得更容易。1.4 版本已经发布了，以下是本次更新的一些亮点：基于 GPU 支持的深度神经网络图像分类(GA)在 .NET 中实现完整的 DNN 模型重新训练和传输学习。例如，你可以通过使用自己的图像从 ML.NET API 中本地培训 TensorFlow 模型来创建自己的自定义图像分类模型。ML.NET 的优点是使用了一个非常简单的高级 API...
We are excited to announce ML.NET 1.4 Preview and updates to Model Builder and 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) and CLI to make it super easy to build custom Machine Learning (ML) models using Automated Machine Learning (AutoML).
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 (ML) models using Automated Machine Learning (AutoML).
Today we’re announcing ML.NET 1.1 which includes updates for ML.NET (v1.0 was released on May 2019) and Model Builder for Visual Studio. Following are the key highlights...
In this article we will use ML.NET to build and compare four Machine Learning Binary Classification pipelines. Each model uses another algorithm to predict the quality of wine from 11 physicochemical features. The characteristics of the prediction models are visualized using OxyPlot. All the code is in C# (“Look mom, no Python!”) and hosted in a UWP app together with some other ML.NET use cases.
Today, coinciding with //BUILD 2019/ conference, we’re thrilled by launching ML.NET 1.0 release! You can read the official ML.NET 1.0 release announcement Blog Post here and get started at the ML.NET site here. In this blog post I’m providing quite a few additional technical details along with my personal vision that you might find interesting, though.
Clustering is a well known type of unsupervised machine learning algorithm. It is unsupervised since there isn't usually a known label in the data to help the algorithm know how to train on a known value. Instead of training on the data point to see a pattern in how it got a label value, an unsupervised algorithm will find patterns among each of the data points themselves. In this post, I'll go over how to use the clustering trainer in ML.NET.