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Book of the week: Machine Learning Projects for .NET Developers by Mathias Brandewinder
Machine Learning Projects for .NET Developers shows you how to build smarter .NET applications that learn from data, using simple algorithms and techniques that can be applied to a wide range of real-world problems. You’ll code each project in the familiar setting of Visual Studio, while the machine learning logic uses F#, a language ideally suited to machine learning applications in .NET. If you’re new to F#, this book will give you everything you need to get started. If you’re already familiar with F#, this is your chance to put the language into action in an exciting new context.
- What's new in .NET Core RC2 (podcast) by the Eat Sleep Code Podcast.
- Workbooks & Inspector 0.9.0 Released by Aaron Bockover.
I just released "TreeLib", a library of balanced binary trees, including the three standard types (AVL, red-black, and splay, drawn from preexisting, well-tested open source implementations). Optionally, the trees are augmented with rank information, facilitating certain types of statistical queries (e.g. median) as well as various types of sparse mappings. A novelty is that each specialization is derived from a master template through Roslyn transforms that strip out unneeded implementation details. It's on NuGet (search for "TreeLib"), the source is here: https://github.com/programmatom/TreeLib, and docs here: http://programmatom.github.io/TreeLib