wehub-resource-sync
50 lines
1.8 KiB
Markdown
50 lines
1.8 KiB
Markdown
# Machine learning algorithms
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A collection of minimal and clean implementations of machine learning algorithms.
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### Why?
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This project is targeting people who want to learn internals of ml algorithms or implement them from scratch.
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The code is much easier to follow than the optimized libraries and easier to play with.
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All algorithms are implemented in Python, using numpy, scipy and autograd.
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### Implemented:
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* [Deep learning (MLP, CNN, RNN, LSTM)](mla/neuralnet)
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* [Linear regression, logistic regression](mla/linear_models.py)
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* [Random Forests](mla/ensemble/random_forest.py)
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* [Support vector machine (SVM) with kernels (Linear, Poly, RBF)](mla/svm)
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* [K-Means](mla/kmeans.py)
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* [Gaussian Mixture Model](mla/gaussian_mixture.py)
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* [K-nearest neighbors](mla/knn.py)
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* [Naive bayes](mla/naive_bayes.py)
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* [Principal component analysis (PCA)](mla/pca.py)
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* [Factorization machines](mla/fm.py)
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* [Restricted Boltzmann machine (RBM)](mla/rbm.py)
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* [t-Distributed Stochastic Neighbor Embedding (t-SNE)](mla/tsne.py)
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* [Gradient Boosting trees (also known as GBDT, GBRT, GBM, XGBoost)](mla/ensemble/gbm.py)
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* [Reinforcement learning (Deep Q learning)](mla/rl)
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### Installation
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```sh
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git clone https://github.com/rushter/MLAlgorithms
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cd MLAlgorithms
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pip install scipy numpy
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python setup.py develop
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```
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### How to run examples without installation
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```sh
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cd MLAlgorithms
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python -m examples.linear_models
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```
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### How to run examples within Docker
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```sh
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cd MLAlgorithms
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docker build -t mlalgorithms .
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docker run --rm -it mlalgorithms bash
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python -m examples.linear_models
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```
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### Contributing
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Your contributions are always welcome!
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Feel free to improve existing code, documentation or implement new algorithm.
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Please open an issue to propose your changes if they are big enough.
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