RustyML and smartcore
These are competitors offering overlapping functionality—both provide general-purpose ML algorithms and neural networks in pure Rust, so users typically choose one based on performance characteristics, API design, and feature maturity rather than using them together.
About RustyML
SomeB1oody/RustyML
A high-performance machine learning library in pure Rust, offering statistical utilities, ML algorithms and neural networks, and future support for transformer architectures.
This project helps developers build high-performance machine learning models without external dependencies, leveraging Rust's strengths. It takes raw data and configuration parameters as input and outputs trained machine learning models for tasks like classification, regression, clustering, and neural networks. This is intended for backend or systems engineers who need to embed predictive capabilities directly into their Rust applications, especially in performance-critical environments.
About smartcore
smartcorelib/smartcore
A comprehensive library for machine learning and numerical computing. Apply Machine Learning with Rust leveraging first principles.
This is a machine learning library built specifically for developers who write code in Rust. It helps Rust developers integrate common machine learning models, like those for classification, regression, and clustering, directly into their applications. You provide your data as Rust vectors or matrices, and it outputs predictions, groupings, or other analytical results, all within your Rust program.
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