Please log in to watch this conference skillscast.
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.
YOU MAY ALSO LIKE:
- Introduction to Knowledge Graphs (In-Person Workshop) with Howard Knowles (in London on 13th June 2022)
- Introduction to Knowledge Graphs (Online Workshop) with Howard Knowles (Online Workshop on 21st July 2022)
- YOW! Data 2022 (Online Conference on 1st - 2nd June 2022)
- Bazel eXchange: Watch Party (in London on 21st June 2022)
- Shift to Data as a Product, Leverage In-Business Expertise to Scale Analytics (Online Meetup on 22nd May 2022)
- Getting Geospatial Data on The Web (SkillsCast recorded in February 2022)
- Haskell: A Conversion Experience (SkillsCast recorded in November 2021)
Deep Learning with F#: An Experience Report
Faisal Waris
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.