- Dask
- Yellowbrick : sklearn viz 👍
- 데이터 사이언스 질문집
- Arviz : 베이즈 시각화 툴 👍
- Category Encoders : useful
- pyod : detection outlying objects
- InterpretML : open-source python package for training interpretable machine learning models and explaining blackbox systems
- ALIBI : open source Python library aimed at machine learning model inspection and interpretation
- ELI5 : Python library which allows to visualize and debug various Machine Learning models using unified API
- SHAP : game theoretic approach to explain the output of any machine learning model
- Interpretation 관련 논문 : 다있다...!
- awesome machine learning : 여기도 다있다
- Complete Guide to Parameter Tuning in XGBoost
- Complete Machine Learning Guide to Parameter Tuning in Gradient Boosting
- An overview of gradient descent optimization algorithms
- Laurae++ : xgboost/lightgbm : useful !
- Xgboost 하이퍼 파라미터 튜닝 : kaggle 예시
- Bayes Optimization 기초부터 XGB까지 : kaggle 예시
- cs231n 강의
- Image classification 관련 블로그 : 한글, 설명 잘해줬다.
- AutoAugment
- fast autoaugment
- all about the gan : GAN 관련 링크 모음
- how to train a gan : tips
- pytorch gan : gan 논문들 구현
- really awesome gan : gan 관련 자료, 논문
- TextGan
- auto-sklearn
- H2O automl
- NNI : lightweight but powerful toolkit to help users automate Feature Engineering, Neural Architecture Search, Hyperparameter Tuning and Model Compression
- automl paper : 나중에 공부 참고
- AutoML-Zero:Evolving Machine Learning Algorithms From Scratch Review : 참고 블로그
- automl opensource 설명