eriklindernoren/ML-From-Scratch

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

32528 stars · 5432 forks observed · 2026-08-28

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

A Python library providing bare-bones NumPy implementations of fundamental machine learning models and algorithms, from linear regression to deep learning and reinforcement learning. It prioritizes transparency and accessibility over computational efficiency, making it ideal for learning how algorithms work internally.

Use cases

• learn how machine learning algorithms work internally
• understand the math behind neural networks with plain numpy
• study implementations of regression, clustering, and CNNs from scratch
• teach a machine learning course with readable reference code
• explore deep reinforcement learning and genetic algorithms in python
• see how backpropagation and gradient descent are implemented

When to choose

• you want transparent, readable implementations to learn ML fundamentals
• you need educational reference code without heavy framework dependencies
• you're teaching or studying how algorithms like CNNs, k-means, or Q-learning work

When to avoid

• you need production-ready, optimized, or GPU-accelerated ML
• you want a maintained framework with active development and ecosystem support
• you need the latest model architectures or extensive documentation

Facets

library · maturity maintenance

machine-learning deep-learning reinforcement-learning data-science machine-learning deep-learning data-science education python numpy educational from-scratch supervised-learning unsupervised-learning genetic-algorithms algorithms

Member repositories

For agents

markdown · JSON · MCP: product_card(name="eriklindernoren/ML-From-Scratch")