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by nickspag
2739 days ago
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I started ML work in python coming from a .NET background. To echo one of the other comments here- I believe one main reason is due to the iterative nature of data science. You have to sort of re-build completely every time you make a change in .NET. There isn't much of a notebook concept in .NET- a running engine you can query with additional commands/cells/etc, which is beneficial. As well, real-world data feels easier to work with in a more forgiving environment (non-static typing, etc) like Python. However ML.NET is a cool endeavor and as the .NET data prep libraries get more mature we may find some more production benefits from the very typing/compiling system that, while making it difficult to iterate in, provide more stability in the wild. |
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Fsharp is scriptable and has jupyter kernel...