在F＃中使用机器学习的资源 [英] Resources for working with Machine Learning in F#

问题描述

我学习了一门使用Matlab作为原型开发工具的机器学习课程。由于我沉迷于F＃，我想继续使用F＃进行机器学习研究。

我可能希望将F＃用于原型和生产，因此机器学习框架将是一个很好的开始。否则，我可以从一个库集合开始：

• 高度优化的线性代数库


• 统计软件包
  
• 可视化库（允许绘制图表和图表并与之交互）
  
• 并行计算工具箱（类似于Matlab并行计算工具箱）

最重要的资源（对我而言）是书籍，博客文章和在线课程机器学习功能编程语言（F＃/ OCaml / Haskell ...）。

任何人都可以提出这些资源吗？

编辑：

这是一个基于以下答案的摘要：

机器学习框架：

• Infer.NET ：用于图形模型中贝叶斯推断的.NET框架具有良好的F＃支持。


• WekaSharper ：围绕流行的数据挖掘框架Weka的F＃封装。


• Microsoft Sho ：数据的持续环境开发分析（包括矩阵运算，优化和可视化）。

相关库：

• Math.NET Numerics ：内部使用英特尔MKL和AMD ACML for矩阵运算和支持统计功能。 Microsoft Solver Foundation ：一个很好的框架线性编程和优化任务。
  

• FSharpChart ：F＃中一个很好的数据可视化库。

阅读列表：

• 数值计算：很棒以F＃中的机器学习开始，并介绍了使用F＃中的这些数学库进行工作的各种工具和技巧/技巧。 > F＃和数据挖掘博客：这也是来自数值计算章节作者尹竺强烈推荐的。


• Machine Learning in Action在F＃中的示例：Mathias已经将一些Python样本翻译成F＃。它们可在 Github 中获得。


• Hal Daume的主页：Hal在OCaml中编写了大量机器学习库。如果您怀疑函数式编程不适合机器学习，那么您会感到放心。

欢迎任何其他指针或建议。

解决方案

在F＃和机器学习中找不到资源，但这里有几个链接这可能相当有用：

• MSDN上的Numerical Computing 部分是使用F＃中各种数字库的好资源。在机器学习中实现线性代数和其他算法的最先进的库是 Math.NET Numerics 。
  

• 可视化数据部分在MSDN上有一些关于F＃图表的资源。 FSharpChart库现由Carl Nolan维护，他定期在他的博客上发布更新。

还有几个人正在处理相关主题的个人页面：

• 。
• 对机器学习感兴趣的学生）在他的博客上有很多优秀文章。

I have learned a Machine Learning course using Matlab as a prototyping tool. Since I got addicted to F#, I would like to continue my Machine Learning study in F#.

I may want to use F# for both prototyping and production, so a Machine Learning framework would be a great start. Otherwise, I can start with a collection of libraries:

• Highly-optimized linear algebra library
• Statistics package
• Visualization library (which allows to draw and interact with charts, diagrams...)
• Parallel computing toolbox (similar to Matlab parallel computing toolbox)

And the most important resources (to me) are books, blog posts and online courses regarding Machine Learning in a functional programming language (F#/OCaml/Haskell...).

Can anyone suggest these kinds of resource? Thanks.

---

EDIT:

This is a summary based on the answers below:

Machine Learning frameworks:

• Infer.NET: an .NET framework for Bayesian inference in graphical models with good F# support.
• WekaSharper: a F# wrapper around the popular data mining framework Weka.
• Microsoft Sho: a continuous environment development for data analysis (including matrix operations, optimization and visualization) on .NET platform.

Related libraries:

• Math.NET Numerics: internally using Intel MKL and AMD ACML for matrix operations and supporting statistics functions too.

• Microsoft Solver Foundation: a good framework for linear programming and optimization tasks.

• FSharpChart: a nice data visualization library in F#.

Reading list:

• Numerical Computing: It is great for starting with Machine Learning in F# and introduces various tools and tips/tricks for working with these Math libraries in F#.
• F# and Data Mining blog: It is also from Yin Zhu, the author of Numerical Computing chapter, highly recommended.
• F# as a Octave/Matlab replacement for Machine Learning: Gustavo has just started a series of blog posts using F# as the development tool. It's great to see many libraries are plugged in together.
• "Machine Learning in Action" 's samples in F#: Mathias has translated some samples from Python to F#. They are available in Github.
• Hal Daume's homepage: Hal has written a number of Machine Learning libraries in OCaml. You would feel relieved if you were in doubt that functional programming was not suitable for Machine Learning.

Any other pointers or suggestions are also welcome.

解决方案

There isn't a single place to look for resources on F# and machine learning, but here are a couple of links that may be quite useful:

• Numerical Computing section on MSDN is a good resource on using various numerical libraries from F#. The most advanced library that implements linear algebra and other algorithsm useful in machine learning is Math.NET Numerics.

• Visualizing Data section on MSDN has some resources on charting in F#. The FSharpChart library is now maintained by Carl Nolan who regularly posts updates to his blog.

There are also a few personal pages of people who are working on relevant topics:

• Jurgen van Gael (who did PhD in machine learning) contributed to the Math.NET library and you can read about his experience here.

• Yin Zhu who wrote the Numerical Computing chapter on MSDN (and is a PhD student interested in machine learning) has quite a few excellent articles on his blog.

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