Machine Learning

And it’s worthwhile to point out the growth in .NET machine learning projects, both to facilitate interop with other frameworks as well as purpose-built .NET workloads. The Apache Spark library is particularly notable that it works with either C# or F# and code can be deployed to Azure HDI, Amazon’s Elastic MapReduce or Databricks. While .NET gets credit for “playing well with others” by being able to run on Mac and Linux, the transpile and interop story of .NET and particularly F# goes much further than that. While I discuss some F# specific tooling, these links provide a quick view into open source initiatives in the machine learning community.

%[https://github.com/fslaborg]

%[https://github.com/SciSharp]

%[https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet]

%[https://dotnet.microsoft.com/apps/data/spark]

• What The F# is Next (original)
  • Warehouse Full of Cannibals
  • The Wrong Kind of DDD
  • A More Functional Approach
  • Machine Learning
  • Success and Kinesthetic Learning
  • Akka.NET, Actor Model and the Reactive Manifesto
  • Other Learning Projects
  • Coda - Microsoft, The Enterprise & The Open Source Balancing Act