Machine learning algorithms

A collection of minimal and clean implementations of machine learning algorithms.

Why?

This project is targeting people who want to learn internals of ml algorithms or implement them from scratch.
The code is much easier to follow than the optimized libraries and easier to play with.
All algorithms are implemented in Python, using numpy, scipy and autograd.

Implemented:

• [Deep learning (MLP, CNN, RNN, LSTM)] (mla/neuralnet)
• [Linear regression, logistic regression] (mla/linear_models.py)
• [Random Forests] (mla/ensemble/random_forest.py)
• [SVM with kernels (Linear, Poly, RBF)] (mla/svm)
• [K-Means] (mla/kmeans.py)
• [Gaussian Mixture Model] (mla/gaussian_mixture.py)
• [K-nearest neighbors] (mla/knn.py)
• [Naive bayes] (mla/naive_bayes.py)
• [PCA] (mla/pca.py)
• [Factorization machines] (mla/fm.py)
• [Gradient Boosting trees (also known as GBDT, GBRT, GBM, XGBoost)] (mla/ensemble/gbm.py)

TODO:

• t-SNE
• MCMC
• Word2vec
• Adaboost
• HMM
• Restricted Boltzmann machine

Installation

    git clone https://github.com/rushter/MLAlgorithms
    cd MLAlgorithms
    pip install scipy numpy
    pip install .

How to run examples without installation

    cd MLAlgorithms
    python -m examples.linear_models

How to run examples within Docker

    cd MLAlgorithms
    docker build -t mlalgorithms .
    docker run --rm -it mlalgorithms bash
    python -m examples.linear_models

Contributing

Your contributions are always welcome!