Expanding 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.
Recently Microsoft announced ML.NET, a machine learning framework for .NET. This is exciting news. So my mind immediately goes to: how does this look with F#? The current post will take a look at using ML.NET’s regression module to predict concrete compressive strength based on its composite ingredients.
Often the question arises, what is the best way to manage environment variables for a project. Many languages have libaries precisely for this issue. A common approach is the use of a
.env file, .NET and F# are no different. In today’s post I’ll take a brief look at leveraging DotEnv in an F# application.
Code comes in many shapes and sizes. Often this means differing paradigms with text or gui workflow tools, but there are other ways. Enter Evo, a robot that leverages coding in the form of color. I recently had the opportunity to play with one of the little bots. It is intended as an educational toy for kids, and it fits the bill perfectly. More than that, it is an interesting case study in seeing the world differently. Take code beyond text, and into a realm that can easily appeal to kids (young and old).