转自"https://www.52ml.net/21379.html"
Machine learning algorithms
A collection of minimal and clean implementations of machine learning algorithms.
机器学习算法的最小和简洁实现的集合。
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.
该项目面向想要学习机器学习算法内部知识或从头开始实现它们的人们。
与优化的库相比，该代码更易于遵循和使用。
所有算法均使用numpy，scipy和autograd在Python中实现。
Implemented:
- Deep learning (MLP, CNN, RNN, LSTM)
- Linear regression, logistic regression
- Random Forests
- SVM with kernels (Linear, Poly, RBF)
- K-Means
- Gaussian Mixture Model
- K-nearest neighbors
- Naive bayes
- PCA
- Factorization machines
- Gradient Boosting trees (also known as GBDT, GBRT, GBM, XGBoost)
TODO:
- t-SNE
- MCMC
- Word2vec
- Adaboost
- HMM
- Restricted Boltzmann machine