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https://github.com/jphall663/automl_resources

A running list of links for AutoML - very unofficial and incomplete
https://github.com/jphall663/automl_resources

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A running list of links for AutoML - very unofficial and incomplete

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# automl_resources

A running list of links for AutoML - very unofficial and incomplete.

### References

* [Deep Feature Synthesis: Towards Automating Data Science Endeavors](https://groups.csail.mit.edu/EVO-DesignOpt/groupWebSite/uploads/Site/DSAA_DSM_2015.pdf)
CSAIL, MIT

* [Efficient and Robust Automated Machine Learning](https://pdfs.semanticscholar.org/775a/4e375cc79b53b94e37fa3eedff481823e4a6.pdf)
Department of Computer Science, University of Freiburg, Germany

* [Using Machine Learning to Explore Neural Network Architecture](https://research.googleblog.com/2017/05/using-machine-learning-to-explore.html)
Google

* [AutoML: Automatic Machine Learning](http://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html)
H2O.ai

### Software

#### Data Exploration

* [Pandas Profiler](https://github.com/pandas-profiling/pandas-profiling)

#### Feature Engineering

* [Feature Tools](https://index.pocketcluster.io/featuretools-featuretools.html)

#### Modeling

* [Auto-Sklearn](https://automl.github.io/auto-sklearn/stable/)
* [H2O.ai AutoML](https://www.h2o.ai/download/)

#### Pipelines

* [TPOT](https://github.com/rhiever/tpot)
* [ATM](https://github.com/HDI-Project/ATM)