AutoML Pkgs

출처 :http://statwith.com/automl-%ea%b4%80%eb%a0%a8-%ed%8c%a8%ed%82%a4%ec%a7%80-%eb%a6%ac%ec%8a%a4%ed%8a%b8/?fbclid=IwAR2arHRh2b1q9dqUO3JqST7iGCR-MLLXMY8L01BBAMdqfvVzimZM9QS7un8

 AutoML.org

• https://www.automl.org/

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1. MLBox

• https://mlbox.readthedocs.io/en/latest/

2. Auto-Sklearn

• https://automl.github.io/auto-sklearn/master/

3. Tree-Based Pipeline Optimization Tool (TPOT)

• http://epistasislab.github.io/tpot/

4. H2O

• http://docs.h2o.ai/?_ga=2.180986341.338131517.1554719885-118242249.1554479889

5. AutoKeras

• https://autokeras.com/

6. Cloud AutoML

• https://cloud.google.com/automl/

7. TransmogrifAI

• https://transmogrif.ai/

8. BOHB (Robust Bayesian Optimization framework)

• https://github.com/automl/HpBandSter

9. BOAH: BAYESIAN OPTIMIZATION & ANALYSIS OF HYPERPARAMETERS

• https://www.automl.org/automl/boah/

10. Amazon Lex

• https://aws.amazon.com/ko/sagemaker/

11. AutoFolio

• https://github.com/mlindauer/AutoFolio

12. Flexfolio

• https://bitbucket.org/mlindauer/flexfolio/src/master/

13. Auto-Pytorch

• https://github.com/automl/Auto-PyTorch

14. Auto-Weka

• http://www.cs.ubc.ca/labs/beta/Projects/autoweka/

15. SMAC (sequential model-based algorithm configuration)

• https://github.com/automl/SMAC3
• https://www.automl.org/automated-algorithm-design/algorithm-configuration/smac/

16. ROBO

• https://github.com/automl/RoBO

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* 11 Most Amazing AutoML Tools To Automate The Machine Learning

• https://techgrabyte.com/11-automl-tools-automate-machine-learning/?fbclid=IwAR36iu6VJK9WFCAY1hsaVbMDh_08Hv-Sps7ZzFVYv0gtRrAmLILKEd8Hru8

* AutoML: The Next Wave of Machine Learning

• https://heartbeat.fritz.ai/automl-the-next-wave-of-machine-learning-5494baac615f

* “데이터 과학자 없는 머신러닝” AutoML의 이해

• http://www.itworld.co.kr/news/129362#csidx08886df4c201319933a3e35946d37f6 

 

AutoGluon

출처 : https://towardsdatascience.com/autogluon-deep-learning-automl-5cdb4e2388ec

AutoGluon: Deep Learning AutoML