What’s new with ML.NET Automated ML (AutoML) and tooling
ML.NET is an open-source, cross-platform machine learning framework for .NET developers that enables integration of custom machine learning into .NET apps. We are excited to update you on what we’ve been working on over the past few months.
ML.NET Recommendation Engine: Pitfall of One-Class Matrix Factorization
During the weekends I decided to take a look at what ML.NET can propose in the area of recommendation engine.I found a nice picture in Mark Farragher’s blog postthat explains three available options...
ML.NET September Updates
ML.NET is an open-source, cross-platform machine learning framework for .NET developers. It enables integrating machine learning into your .NET apps without requiring you to leave the .NET ecosystem or even have a background in ML or data science. ML.NET provides tooling (Model Builder UI in Visual Studio and the cross platform ML.NET CLI) that automatically trains custom machine learning models for you based on your scenario and data. This release of ML.NET (1.5.2) brings numerous bug fixes and enhancements, while tooling updates include the ability to train object detection models using Azure ML via Model Builder. You can now also locally train image classification models with the ML.NET CLI.
August ML.NET API and Tooling Updates
ML.NET is an open-source, cross-platform machine learning framework for .NET developers. It enables integrating machine learning into your .NET apps without requiring you to leave the .NET ecosystem or even have a background in ML or data science. ML.NET provides tooling (Model Builder UI in Visual Studio and the cross platform ML.NET CLI) that automatically trains custom machine learning models for you based on your scenario and data.
Using ML.NET for deep learning on images in Azure
This post will show how to train a custom image classification model in Azure to categorize flowers using ML.NET Model Builder. Then, you can leverage your existing .NET skills to consume the trained model inside a C# .NET Core console application. Best of all, little to no prior machine learning knowledge is required. Let’s get started!
ML.NET 1.4 发布，跨平台机器学习框架
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...
Announcing ML.NET 1.4 Preview and Model Builder updates (Machine Learning for .NET)
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).
ML.NET 1.3.1 发布，.NET 跨平台机器学习框架
ML.NET 1.3.1 已发布。ML.NET 是一个跨平台的机器学习框架，旨在让 .NET 开发者更快上手机器学习，它允许 .NET 开发者开发他们自己的模型，并将自定义 ML 注入到应用程序中。
Announcing ML.NET 1.2 and Model Builder updates (Machine Learning for .NET)
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).
ML.NET 1.1 发布，模型构建器升级和新的异常检测算法
ML.NET 1.1 已发布。ML.NET 是一个跨平台的机器学习框架，旨在让 .NET 开发者更快上手机器学习，它允许 .NET 开发者开发他们自己的模型，并将自定义 ML 注入到应用程序中。1.1 的更新亮点包括针对 ML.NET 的更新，以及用于 Visual Studio 的 Model Builder 的更新。...
Announcing ML.NET 1.1 and Model Builder updates (Machine Learning for .NET)
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...
Machine Learning with ML.NET in UWP: Binary Classification
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.
ML.NET 1.0 发布，单击右键即可添加机器学习模型
ML.NET 1.0 终于发布了。ML.NET 是一个跨平台的机器学习框架，旨在让 .NET 开发者更快上手机器学习，它允许 .NET 开发者开发他们自己的模型，并将自定义 ML 注入到应用程序中。
What is ML.NET 1.0 - Machine Learning for .NET
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 in ML.NET
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.
基于C#的机器学习--面部和动态检测-图像过滤器
在本章中，我们将展示两个独立的例子，一个用于人脸检测，另一个用于动态检测，以及如何快速地将这些功能添加到应用程序中。 在这一章中，我们将讨论:面部检测动态检测将检测添加到应用程序中面部检测 人脸检测，是人脸识别的第一部分。如果你不能从屏幕上的所有东西中识别出一个或多个人脸，那么你将永远无法识别那是谁的脸。 首先让我们看一张我们的应用程序截图:...
ML.NET 0.10特性简介
IDataView被单独作为一个类库包IDataView组件为表格式数据提供了非常高效的处理方式，尤其是用于机器学习和高级分析应用。它被设计为可以高效地处理高维数据和大型数据集。并且也适合处理属于更大的分布式数据集中的单个数据区块结点。在ML.NET 0.10中，IDataView被拆分成单个程序集和NuGet类库包。这对于与其它API及框架交互是极重要的一步。在被拆分后，其它的类库将能直...
ML.NET 0.9特性简介
ML.NET 0.9已于上周发布，距离上次0.8版本的发布只有一个多月，此次增加的新特性主要包括特征贡献计算，模型可解释性增强，ONNX转换对GPU的支持，Visual Studio ML.NET项目模板预览，以及API改进。特征贡献计算特征贡献计算(Feature Contribution Calculation)通过决定每个特征对模型分数的贡献，从而显示哪些特征在对特别个体的数据样本的模型...
ML.NET教程之出租车车费预测(回归问题)
理解问题出租车的车费不仅与距离有关，还涉及乘客数量，是否使用信用卡等因素(这是的出租车是指纽约市的)。所以并不是一个简单的一元方程问题。准备数据建立一控制台应用程序工程，新建Data文件夹，在其目录下添加taxi-fare-train.csv与taxi-fare-test.csv文件，不要忘了把它们的Copy to Output Directory属性改为Copy if newer。之后，添加...
撒花！中文翻译仓库链接已加入 ML.NET 官方示例网站首页
撒花！中文翻译仓库链接已加入 ML.NET 官方示例网站首页从2018年12月02日决定开始做ML.NET 示例中文版https://github.com/feiyun0112/machinelearning-samples.zh-cn，然后以每天一篇的速度进行翻译，总共耗时15天，将现有的官方实例全部翻译成了中文，并提交了添加中文链接PR，现已合并到https://github.com/dotn...