ML.NET is an open source and cross-platform machine learning framework
https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet
Machine Learning made for .NET
ML.NET is a machine learning framework built for .NET developers.
Use your .NET and C# or F# skills to easily integrate custom machine learning into your applications without any prior expertise in developing or tuning machine learning models.
Open source and cross-platform
ML.NET is open source and runs on Windows, Linux, and macOS.
Our public release is still in-development, and we want your help! Join the community and contribute your ideas to help us shape what comes next.
Proven and extensible
Use the same framework behind recognized Microsoft features like Windows Hello, Bing Ads, and PowerPoint Design Ideas to power your own applications.
We're building ML.NET as an extensible framework, with support for Light GBM, Accord.NET, CNTK, and TensorFlow coming soon.
Cover your developer scenarios
Enhance your .NET apps with sentiment analysis, price prediction, fraud detection, and more using custom models built with ML.NET.
ML.NET is an open source and cross-platform machine learning framework的更多相关文章
- [Machine Learning & Algorithm]CAML机器学习系列1:深入浅出ML之Regression家族
声明:本博客整理自博友@zhouyong计算广告与机器学习-技术共享平台,尊重原创,欢迎感兴趣的博友查看原文. 符号定义 这里定义<深入浅出ML>系列中涉及到的公式符号,如无特殊说明,符号 ...
- V4 Reduce Transportable Tablespace Downtime using Cross Platform Incremental Backup (Doc ID 2471245.1)
V4 Reduce Transportable Tablespace Downtime using Cross Platform Incremental Backup (Doc ID 2471245. ...
- Gtest:Using visual studio 2017 cross platform feature to compile code remotely
参考:使用Visual Studio 2017作为Linux C++开发工具 前言 最近在学Gtest单元测试框架,由于平时都是使用Source Insight写代码,遇到问题自己还是要到Linux下 ...
- [Machine Learning & Algorithm]CAML机器学习系列2:深入浅出ML之Entropy-Based家族
声明:本博客整理自博友@zhouyong计算广告与机器学习-技术共享平台,尊重原创,欢迎感兴趣的博友查看原文. 写在前面 记得在<Pattern Recognition And Machine ...
- PredictionIO Open Source Machine Learning Server
PredictionIO Open Source Machine Learning Server Build Smarter Software with Machine Learning Predic ...
- 课程三(Structuring Machine Learning Projects),第一周(ML strategy(1)) —— 0.Learning Goals
Learning Goals Understand why Machine Learning strategy is important Apply satisficing and optimizin ...
- Google's Machine Learning Crash Course #01# Introducing ML & Framing & Fundamental terminology
INDEX Introducing ML Framing Fundamental machine learning terminology Introducing ML What you learn ...
- [ML] I'm back for Machine Learning
Hi, Long time no see. Briefly, I plan to step into this new area, data analysis. In the past few yea ...
- “CMake”这个名字是“cross platform make”
cmake_百度百科 https://baike.baidu.com/item/cmake/7138032?fr=aladdin CMake 可以编译源代码.制作程序库.产生适配器(wrapper). ...
随机推荐
- 微信小程序异步请求问题
微信小程序为了提高用户体验,提供的api大部分都是异步操作,除了数据缓存操作里面有一些同步操作.是提高了用户体验,但是在开发的时候, 就有点坑了,例如我要写一个公共方法,发起网络请求,去后台去一些数据 ...
- alfs学习笔记-安装和使用blfs工具
我的邮箱地址:zytrenren@163.com欢迎大家交流学习纠错! 一名linux爱好者,记录构建Beyond Linux From Scratch的过程 经博客园-骏马金龙前辈介绍,开始接触学习 ...
- 【代码笔记】Web-CSS-CSS 语法
一,效果图. 二,代码. <!DOCTYPE html> <html> <head> <meta charset="utf-8"> ...
- Microsoft Dynamics CRM 9.0 OP 版本 安装 的那些 雷
天天讲安装过程好无聊了,还是搞点有营养的东西来,那么后面来说说刚出来的MSCRM OP 9.0 版本安装的那些雷: 雷1:操作系统要求Windows 2016 Server 这点还好,因为之前安装MS ...
- 什么是基于风险的测试(RBT)?
基于风险的测试(Risk-based testing) 文/杨学明 一.基于风险的测试起源 基于风险的测试起源,在软件测试领域,基于风险测试最早的是测试大师Boris Beizer<软件测试技术 ...
- Docker 创建 Confluence6.12.2 中文版
目录 目录 1.介绍 1.1.什么是Confluence? 2.Confluence的官网在哪里? 3.如何下载安装? 4.对 Confluence 进行配置 4.1.设置 Confluence 4. ...
- lcd参数解释及刷新率计算,LCD时序
一.LCD显示图像的过程如下: 其中,VSYNC和HSYNC是有宽度的,加上后如下: 参数解释: HBP(Horizontal Back Porch)水平后沿:在每行或每列的象素数据开始输出时要插入的 ...
- AXI-Lite总线及其自定义IP核使用分析总结
ZYNQ的优势在于通过高效的接口总线组成了ARM+FPGA的架构.我认为两者是互为底层的,当进行算法验证时,ARM端现有的硬件控制器和库函数可以很方便地连接外设,而不像FPGA设计那样完全写出接口时序 ...
- 统计numpy数组中最频繁出现的值
arr = np.array([[1,2,100,4,5,6],[1,1,100,3,5,5],[2,2,4,4,6,6]]) 方法一: count = np.bincount(arr[:,2]) # ...
- Dijango学习_02_极简本地博客创建
二. Python 自带SQLite3数据库,Django默认使用SQLite3数据库,如果使用其它数据库可以在settings.py文件中设置. DATABASES = { 'default': { ...