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This talk details the experience of using F# for DL and data science (DS) on a daily basis in two large corporations and compares F# with Python for DL/DS
With respect to DS/DL, there are two issues of concern with Python: (A) models are becoming tightly integrated with the front-end language (e.g. Python and PyTorch). This makes its harder to consume some recent models from other languages / ecosystems. And (B) today's data pipelines, can become large and complex resulting in large code bases surrounding the actual DL model(s). Python's lack of static typing and slowness is a major impediment.
F# is expressive, statically type-checked and much faster than basic Python at data handling. Combine that with tooling that supports interactive programming well an F# is ideally suited DS/DL for the .Net ecosystem.
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ML/AI researcher and data scientist for over 5 years of experience in data science and deep learning. PhD in Computer Science from Wayne State University.