eriklindernoren

    eriklindernoren/ML-From-Scratch

    Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

    33.2K stars
    5.5K forks
    33.2K GitHub watchers
    Updated Nov 2, 2026
    View on GitHub

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    Where it stands

    Rank, growth pace and release cadence relative to everything GStars tracks.

    The project lists 6 contributors on GitHub.

    Peaked at #96 in the weekly ranking the week of Sep 7, its best in the past year.

    Use Cases & Benefits

    • Implements fundamental machine learning models and algorithms from scratch using Python and NumPy for educational transparency.
    • Provides clear, accessible code to understand inner workings of ML algorithms rather than optimized performance.
    • Use for learning and experimenting with core supervised and unsupervised machine learning techniques in Python.
    • Use for exploring deep learning architectures like CNNs, GANs, and reinforcement learning with simple, readable implementations.
    • Use for studying evolutionary algorithms and genetic algorithms applied to neural networks and optimization problems.

    Should you depend on it?

    License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.

    License

    MIT

    Permissive: commercial use, modification and redistribution are allowed with attribution.

    Community files

    • README: present

    Watch-outs

    • Warning: No commits in over a year (last push Oct 2023).
    • No GitHub releases; pin a commit or tag instead of a release.

    General guidance from repository metadata, not legal advice. How we score

    From the README

    Python implementations of some of the fundamental Machine Learning models and algorithms from scratch.

    The purpose of this project is not to produce as optimized and computationally efficient algorithms as possible but rather to present the inner workings of them in a transparent and accessible way.

    Excerpt from the eriklindernoren/ML-From-Scratch README on GitHub (MIT license). © its authors.Read the full README on GitHub

    Daily snapshots

    Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots

    Sep 5: +15Sep 6: +87Sep 7: +18Sep 8: +19Sep 9: +35Sep 10: +13Sep 11: +1

    +840 stars over 90 days across 14 snapshot days; best day +87 on Sep 6.

    Forks

    5.5K+109

    Open issues

    82+6

    Compared with peers in Deep Learning

    Fastest-growing repositories GStars tracks in this cohort for the week of Nov 8, by 7-day star growth

    eriklindernoren/ML-From-Scratch compared with Deep Learning repositories by stars, 7-day star growth, health score and last release
    RepositoryStars+7dHealthLast release
    rohitg00/ai-engineering-from-scratch78.3K+1.6K751 day ago
    eriklindernoren/ML-From-Scratch33.2K—9—
    SYSTRAN/faster-whisper26.5K—40368 days ago
    GeeeekExplorer/nano-vllm16.3K—12—
    Lightning-AI/litgpt13.8K—58129 days ago
    data-privacy-stack/presidio11.8K—6410 days ago
    Category median16.3K—58—

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