aprender 0.11.0

Next-generation machine learning library in pure Rust
Documentation 
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# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: "aprender: Next Generation Machine Learning in Pure Rust"
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Noah
    family-names: Gift
    email: noah.gift@gmail.com
    affiliation: Paiml
    orcid: 'https://orcid.org/0000-0003-1234-5678'
identifiers:
  - type: url
    value: 'https://github.com/paiml/aprender'
    description: GitHub Repository
repository-code: 'https://github.com/paiml/aprender'
url: 'https://paiml.github.io/aprender/'
repository-artifact: 'https://crates.io/crates/aprender'
abstract: >-
  Aprender is a lightweight, pure Rust machine learning library
  designed for efficiency and ease of use. Built with EXTREME TDD
  methodology, it provides reliable implementations of core ML
  algorithms including Linear Regression, Logistic Regression,
  Decision Trees, Random Forests, Gradient Boosting, K-Means,
  PCA, and more. Features include type-safe AutoML with TPE
  optimization, comprehensive test coverage (95%+), and
  SIMD acceleration via trueno.
keywords:
  - machine-learning
  - rust
  - ml
  - statistics
  - deep-learning
  - pure-rust
  - tdd
  - automl
  - clustering
  - classification
  - regression
license: MIT
version: 0.10.0
date-released: '2024-11-26'
references:
  - type: software
    title: trueno
    authors:
      - given-names: Noah
        family-names: Gift
    repository-code: 'https://github.com/paiml/trueno'
    abstract: SIMD-accelerated tensor operations for Rust
  - type: article
    title: "Algorithms for Hyper-Parameter Optimization"
    authors:
      - family-names: Bergstra
        given-names: James
      - family-names: Bardenet
        given-names: Rémi
      - family-names: Bengio
        given-names: Yoshua
      - family-names: Kégl
        given-names: Balázs
    journal: "Advances in Neural Information Processing Systems"
    year: 2011
    notes: >-
      TPE (Tree-structured Parzen Estimator) algorithm
      implemented in aprender's AutoML module
  - type: article
    title: "Random Search for Hyper-Parameter Optimization"
    authors:
      - family-names: Bergstra
        given-names: James
      - family-names: Bengio
        given-names: Yoshua
    journal: "Journal of Machine Learning Research"
    year: 2012
    volume: 13
    pages: "281-305"
    notes: >-
      Random search baseline implemented in aprender's AutoML module