We're sorry but this page doesn't work properly without JavaScript enabled. Please enable it to continue.
Feedback

Machine Learning with F#

Formal Metadata

Title
Machine Learning with F#
Alternative Title
F# and Machine Learning: a winning combination
Title of Series
Number of Parts
170
Author
License
CC Attribution - NonCommercial - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
Identifiers
Publisher
Release Date
Language

Content Metadata

Subject Area
Genre
Abstract
While Machine Learning practitioners routinely use a wide range of tools and languages, C# is conspicuously absent from that arsenal. Is .NET inadequate for Machine Learning? In this talk, I'll argue that it can be a great fit, as long as you use the right language for the job, namely F#. F# is a functional-first language, with a concise and expressive syntax that will feel familiar to data scientists used to Python or Matlab. It combines the performance and maintainability benefits of statically typed languages, with the flexibility of Type Providers, a unique mechanism that enables seamless consumption of virtually any data source. And as a first-class .NET citizen, it interops smoothly with C#. So if you are interested in a language that can handle both flexible data exploration and the pressure of a real production system, come check out what F# has to offer.