NaoMatch/FortLearner
Machine Learning Algorithms in Fortran
This project provides extremely fast machine learning algorithms for clustering data. You give it your raw numerical data, and it groups similar data points together, telling you which group each point belongs to. This is ideal for data scientists or analysts who need to quickly process large datasets for tasks like customer segmentation or anomaly detection.
No commits in the last 6 months.
Use this if you are a data scientist working with large numerical datasets and need a high-performance solution for K-Means clustering, especially on Linux systems.
Not ideal if you are looking for a broad suite of machine learning algorithms beyond K-Means, or if you primarily work on macOS or Windows and prefer not to compile software from source.
Stars
31
Forks
7
Language
Fortran
License
MIT
Category
Last pushed
Sep 25, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/NaoMatch/FortLearner"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
mlverse/torch
R Interface to Torch
modern-fortran/neural-fortran
A parallel framework for deep learning
Beliavsky/Fortran-code-on-GitHub
Directory of Fortran codes on GitHub, arranged by topic
Cambridge-ICCS/FTorch
A library for directly calling PyTorch ML models from Fortran.
NVIDIA/TorchFort
An Online Deep Learning Interface for HPC programs on NVIDIA GPUs