rushter/MLAlgorithms
A Python library of clean, educational implementations of common machine learning algorithms including neural networks, ensemble methods, and reinforcement learning.
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This repository provides minimal, readable implementations of machine learning algorithms designed for learning purposes. It covers neural network architectures (MLP, CNN, RNN, LSTM), classical algorithms (SVM, K-means, PCA, Naive Bayes), ensemble methods (Random Forest, Gradient Boosting), and deep reinforcement learning (Deep Q-learning). All implementations use numpy, scipy, and autograd, prioritizing clarity over optimization.