ddbourgin/numpy-ml

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

16330 stars · 3742 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

numpy-ml is a collection of machine learning models and algorithms implemented exclusively in NumPy and the Python standard library, covering neural networks, HMMs, GMMs, topic models, tree-based models, and reinforcement learning. It serves as readable reference implementations for learning, prototyping, and experimentation rather than production use.

Use cases

• learn how machine learning algorithms work by reading clean from-scratch implementations
• implement a neural network in pure numpy without deep learning frameworks
• study reference implementations of LSTM, attention, and ResNet blocks
• prototype ML experiments with minimal dependencies
• understand EM training for Gaussian mixture models and HMMs
• explore reinforcement learning agents on OpenAI gym environments
• implement topic modeling or word2vec from scratch

When to choose

• you want legible, dependency-light implementations to read and modify
• you're teaching or learning ML fundamentals and want to see the math in code
• you need a starting point for rapid ML prototyping without heavy frameworks
• you want to benchmark your own implementation against a simple reference

When to avoid

• you need production performance or GPU acceleration
• you're building a real application and should use PyTorch, TensorFlow, or scikit-learn
• you need well-tested, bug-free software - the project explicitly disclaims guarantees
• you need comprehensive documentation - docs are still under development

Facets

library · maturity maintenance

machine-learning deep-learning nlp reinforcement-learning data-science machine-learning deep-learning education python cross-platform numpy reference-implementations educational from-scratch neural-networks topic-modeling hidden-markov-models gaussian-mixture-models reinforcement-learning no-framework-dependencies natural-language-processing algorithms

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