MachineLearningBasicCodes🏆
朱子云：
所谓致知在格物者，言欲致吾之知，在即物而穷其理也。盖人心之灵，莫不有知，而天下之物，莫不有理。惟于理有未穷，故其知有不尽也。是以大学始教，必使学者即凡天下之物，莫不因其已知之理而益穷之，以求至乎其极。至于用力之久，而一时豁然贯通焉，则众物之表里精粗无不到，而吾心之全体大用无不明矣。
📐📏
格物 (Ko Wu) which means 'investigate the essence of things' in English is a key method for study and better understanding of the knowledge. It is proposed by ancient Chinese philosophers about 2000 years ago and has a profound impact on later generations. The spirit of Ko Wu asks us to not only learn how to use knowledge, but also clearly understand the intrinsic theory. Therefore, it is necessary to reimplement ML algorithms by ourselves to figure out what exactly they did and why they succeed.
This repository aims to implement popular Machine Learning and Deep Learning algorithms by both pure python and use opensource frameworks.
 Common Machine Learning Part: switch by
use_sklearn
flag in the main function；
 Deep Learning Part: four implement methods for each algorithm (
use_sklearn
, use_keras
, use_torch
and self_implement
)；
 Applications Part: RL + NLP + CV
 New trend: GNNs
Welcome everyone to help me finish this Ko Wu project by pulling requests or giving me some suggestions and issues!!!
关联知乎专栏 Associated Zhihu Blog
RL in Robotics
Machine Learning 格物志
代码目录 Code Catalog
Regression
 Single Linear Regression
 Multiple Linear Regression
Classification
 Logistic Regression
 KNN
 Support Vector Machine
 Naive Bayes
Regression & Classification
 Decision Tree
 Random Forest
Neural Network
 Feedforward Neural Network
 Convolutional Neural Network
 LSTM
Unsupervised Learning
 PCA
 KMeans
Ensemble Model
 Boosting
Reinforcement Learning

Value Based Methods: Qlearning(Tabular), DQN

Policy Based Methods: Vanilla Policy Gradient, TRPO, PPO

ActorCritic Structure: AC, A2C, A3C

Deep Deterministic Policy Gradient: DDPG, DDPG C++ (Undone), TD3
 Soft ActorCritic
Computer Vision
 GAN

Resnet: Pytorch version, libtorch C++ version
Natural Language Processing
 Attention mechanism
 Transformer
 BERT
Graph Neural Networks
 Graph Neural Network (GNN)
 Graph Convolutional Neural Network (GCN)
 Graph Attention Networks (GAT)
 GraphSAGE
 GraphRNN
 Variational Graph AutoEncoders (GAE)