FLearn 1.1.1
Machine Learning made simple.
See the version list below for details.
Install-Package FLearn -Version 1.1.1
dotnet add package FLearn --version 1.1.1
<PackageReference Include="FLearn" Version="1.1.1" />
paket add FLearn --version 1.1.1
FLearn
Definition:
- To learn through failure. I flearnt how to ski by sliding face first down a mountain.
- A machine learning library written in F# for F#. Woah, FLearn flearnt that pattern really quickly!
Built on top of the Math.Net Numerics linear algebra library, FLearn offers an extensible, efficient and interactive take on machine learning. Utilizing tools from online convex optimization, FLearn provides a framework for quickly specifying and optimizing various predictive models. Straight out of the box you can:
- Construct powerful predictive models, with statistical and worst case guarantees, via the minimization of convex loss functions.
- Utilize powerful feature maps such as those provided by random Fourier features and the Nystrom method.
- Make predictions and estimate their error! Think error bars and Gaussian processes.
- Efficiently Cross-validate different models via bandit algorithms.
- Learn from noisily labeled data via the method of unbiased estimators and corruption corrected loss functions.
- Extend the framework, adding new loss functions and feature maps as you see fit!
EXAMPLES INCOMING.......
FLearn
Definition:
- To learn through failure. I flearnt how to ski by sliding face first down a mountain.
- A machine learning library written in F# for F#. Woah, FLearn flearnt that pattern really quickly!
Built on top of the Math.Net Numerics linear algebra library, FLearn offers an extensible, efficient and interactive take on machine learning. Utilizing tools from online convex optimization, FLearn provides a framework for quickly specifying and optimizing various predictive models. Straight out of the box you can:
- Construct powerful predictive models, with statistical and worst case guarantees, via the minimization of convex loss functions.
- Utilize powerful feature maps such as those provided by random Fourier features and the Nystrom method.
- Make predictions and estimate their error! Think error bars and Gaussian processes.
- Efficiently Cross-validate different models via bandit algorithms.
- Learn from noisily labeled data via the method of unbiased estimators and corruption corrected loss functions.
- Extend the framework, adding new loss functions and feature maps as you see fit!
EXAMPLES INCOMING.......
Dependencies
-
- FSharp.Core (>= 4.2.3)
- MathNet.Numerics.FSharp (>= 3.20.0)
GitHub Usage
This package is not used by any popular GitHub repositories.
