Occasionally the need arises in an F# project to perform benchmarking. BenchmarkDotNet is a powerful tool made exactly for this purpose. Today’s post provides an introductory look into the process.
F# and ML.NET Clustering (V2)
Read Time: 14 minutesWith the release of v0.7.0, it is time to revisit K-means clustering using F# and Microsoft’s new ML.NET framework. The api has changed enough to warrant a minor rework. This post is a re-examination of a previous post F# and ML.NET Clustering. The use case will be to use examination attributes to classify mammogram results.
F# and Elasticsearch
Read Time: 8 minutesRecently I was working on a project using F# and Elasticsearch. I thought it would be fun to post a light introduction. Nicely enough, Elastic provides a .NET client, two actually here. They are a combination of low level and high level interfaces. As is sometimes the case, using F# and C#-style libraries requires some clever navigation of the interface. For this post I will only focus on the high level interface (NEST).
F# and ML.NET Clustering
Read Time: 13 minutesF# and ML.NET Classification
Read Time: 12 minutesExpanding on my previous post, F# and ML.NET Regression, the current post will take a look at performing classification using Microsoft’s new ML.NET framework. The task at hand will be to use biomechanical attributes to classify patient vertebra conditions into normal (NO), disk hernia (DH), or spondilolysthesis (SL) categories.