numpy-ml is a collection of common machine learning algorithms implemented exclusively in NumPy. It is designed to be a transparent reference and starting point for educational purposes and rapid prototyping rather than optimized production use.
Project overview
By prioritizing legibility over efficiency and implementing a wide range of algorithms purely in NumPy, numpy-ml provides a uniquely accessible foundation for understanding and customizing ML architectures.
Project type
Workflow · Model Development · Data Processing · Infrastructure
Deployment
Refer to project documentation
License
GPL-3.0
Best for
Educators and developers seeking transparent, readable machine learning algorithm implementations for learning and rapid prototyping.
Key capabilities
Includes Gaussian mixture models trained via Expectation-Maximization.
Provides Hidden Markov models featuring Viterbi decoding, likelihood computation, and MLE parameter estimation via Baum-Welch.
Offers neural network layers including LSTM and Conv1D/2D, modules such as ResNet, Transformer, and WaveNet, alongside regularizers, normalization methods, optimizers like Adam and RMSProp, and loss functions.
Contains various linear models including ridge regression, logistic regression, ordinary least squares, and Bayesian linear regression.
Includes reinforcement learning agents such as cross-entropy method, Monte Carlo, Expected SARSA, TD-0 Q-learning, and Dyna-Q.
Provides decision trees via CART, random forests, and gradient-boosted decision trees.
Features discrete Fourier transform, DCT, MFCC encoding, TF-IDF, and Byte pair encoding utilities.
Limitations and risks
The implementations are intentionally inefficient and intended as a starting point rather than highly optimized production code.
Designed for rapid experimentation and prototyping, which may indicate a lack of robustness or completeness for production environments.
Getting started
Install the project easily by cloning the repository, creating a virtualenv, and installing the development requirements using pip3.
Evidence and sources
README: Ever wish you had an inefficient but somewhat legible collection of machine learning algorithms implemented exclusively in NumPy? No?
README: To use this code as a starting point for ML prototyping / experimentation, just clone the repository, create a new virtualenv, and start hacking: