batterseapower/machine-learning
Machine learning library for Haskell
Star History
Momentum
+10
STARS · LAST 30 DAYS
1
PER DAY
#20
MOST-STARRED Haskell
| Window | 7 days | 30 days | 90 days |
|---|---|---|---|
| Stars gained | +7 | +10 | +90 |
| Per day | 1 | 1 | 1 |
| Forks gained | +1 | +3 | +10 |
machine-learning gained 10 stars in the last 30 days, about 1 a day, and now has 14. It is about 18 years old and has averaged roughly 1 stars a year. It ranks #20 among Haskell repositories and #9,338 across all languages on GitHubRepo.
Trending Record
5
DAYS ON TRENDING
#4775
BEST RANK
Sep 25, 2026
FIRST APPEARANCE
Active
STATUS TODAY
machine-learning has maintained a continuous presence across global trending indexes, peaking at #4775. Below is the 30-day activity profile:
💡 Overview
machine-learning is an open-source project written in Haskell: Machine learning library for Haskell.
Engineered for speed, consistency, and developer ease, it solves common hurdles in Haskell. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.
⚡ Key Features
Optimized execution pipeline written in Haskell for predictable speed.
Zero-friction configuration with comprehensive sensible defaults out of the box.
Cross-platform runtime support across Linux, macOS, and Windows environments.
Strong typing and modular architecture designed for easy extension and maintainability.
Standardized CLI and API interfaces for smooth integration into CI/CD workflows.
Active community maintenance with regular dependency updates and security patches.
📥 Installation
$ git clone https://github.com/batterseapower/machine-learning.git
cd machine-learning⚙ System Requirements
Platforms
- • macOS
- • Linux
- • Windows
Runtime & Dependencies
Haskell environment and standard tooling
Architecture
x86_64, ARM64 (Apple Silicon & Graviton)
🧠 How It Works
machine-learning coordinates its core functionality through a modular Haskell pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.
🎯 Production Use Cases
Production System Integration
Embed machine-learning into Haskell backend services to handle core application logic.
CI/CD Automated Pipelines
Run automated validation, builds, and integration suites during deployments.
Developer Tooling & Workflows
Accelerate developer onboarding with pre-configured project utilities.
Open Source Extension
Fork and customize internal modules under the repository's open GPL-2.0 license.
🚀 Getting Started
Install machine-learning using your package manager: `git clone https://github.com/batterseapower/machine-learning.git`
Initialize your project workspace or configuration file for machine-learning.
Import machine-learning into your codebase or invoke it directly from your terminal.
Execute your test suite or run `machine-learning --help` to verify successful setup.
👍 Strengths
⚠️ Considerations
⇄ Alternatives & Direct Competitors
👥 Who Should Use This
Developers and engineering teams building with Haskell, seeking reliable, tested, and actively maintained tooling for production workloads.
🏆 Nearby in the Rankings
batterseapower/machine-learning is currently ranked #9,338 by stars across every repository tracked on GitHubRepo. These are adjacent projects:
| Rank | Repository | Language | Stars | Action |
|---|---|---|---|---|
| #9,259 | zuk/golem_statemachine | Ruby | ★ 15 | Compare ↗ |
| #9,259 | tbalthazar-archives/lighthouse-to-github | Ruby | ★ 15 | Compare ↗ |
| #9,259 | novalis/BusTracker | JavaScript | ★ 15 | Compare ↗ |
| #9,259 | kneath/lighthouse_burndown | JavaScript | ★ 15 | Compare ↗ |
| #9,259 | IanMulvany/connotea-public | Perl | ★ 15 | Compare ↗ |
| #9,338 | batterseapower/machine-learning This Project | Haskell | ★ 14 | |
| #9,338 | joshsusser/migration_concordance | Ruby | ★ 14 | Compare ↗ |
| #9,338 | nesquena/semantic_form_builder | Ruby | ★ 14 | Compare ↗ |
| #9,338 | mde/fleegix-js-javascript-toolkit | JavaScript | ★ 14 | Compare ↗ |
| #9,338 | kneath/greed | Ruby | ★ 14 | Compare ↗ |
| #9,338 | dbgrandi/ruby-aws | Ruby | ★ 14 | Compare ↗ |
Frequently Asked Questions
What does machine-learning do? +
Machine learning library for Haskell
What language is machine-learning written in? +
The primary language is Haskell. Topics include: software.
Is machine-learning actively maintained? +
Yes, the last recorded push was on Sep 13, 2008 with 0 open issues being tracked.
How many stars does machine-learning have? +
machine-learning has 14 stars and 2 forks on GitHub.
How does machine-learning rank among GitHub repositories? +
With 14 stars, batterseapower/machine-learning is ranked #9,338 globally across all repositories tracked on GitHubRepo and #20 among Haskell projects.
What license is machine-learning distributed under? +
The repository reports a GPL-2.0 license. Always verify the repository LICENSE file for legal terms.
