Hands-on Kaggle meetup #21 AutoML 2 : MLBox new release

Dear Members,

Agenda 7pm-7:30 welcome

After some feedback on the current challenges, open discussions
and some hints on the DL summer school in warsaw,

Axel de Romblay and Henri Gérard will present the latest release of MLBox !

0°) Introduction & news since last meetup.

1°) MLBox workshop: quick introduction on Auto-ML + tool overview (repo, features, setup...) + hands-on on a real dataset.

We recommend you to install MLBox before the workshop:

>>> https://github.com/AxeldeRomblay/MLBox

2°) Discussion about the next meetup. One speaker from Deezer is forecasted

3°) Networking , buffet

End 10pm

Please put your remarks and suggestion in the discussion page.

>> materials :

AutoML, : huge list of products coming from GAFA or editors ( like H2O.ai)

Note last one from Google
https://cloud.google.com/automl-tables/docs/quickstart

Also this competition oriented on AutoML :
https://www.kaggle.com/c/ieee-fraud-detection/rules

Book :
https://www.automl.org/book/

===

MLBox
https://github.com/AxeldeRomblay/MLBox

CPMP
https://www.youtube.com/watch?time_continue=6&v=fH_FiquKhiI

Slack channel
https://kagglenoobs.slack.com/

=================== NeurIPS tutorial

https://nips.cc/Conferences/2018/Schedule?showParentSession=12526

Franck Hutter team

=================== From Robert's meetup
Papers:

1. Automated Machine Learning: https://en.wikipedia.org/wiki/Automated_machine_learning

2. Visus: An Interactive System for Automatic Machine Learning Model Building and Curation: https://arxiv.org/abs/1907.02889v1

3. An Open Source AutoML Benchmark: https://arxiv.org/pdf/1907.00909v1.pdf

4. Transfer Learning with Neural AutoML: https://arxiv.org/abs/1803.02780v5

5. A Very Brief and Critical Discussion on AutoML: https://arxiv.org/abs/1811.03822v1

6. Survey on Automated Machine Learning: https://arxiv.org/abs/1904.12054

7. AutoML: Automating the design of machine learning models for autonomous driving: https://medium.com/waymo/automl-automating-the-design-of-machine-learning-models-for-autonomous-driving-141a5583ec2a

8. Neural Architecture Search with Reinforcement Learning: https://arxiv.org/abs/1611.01578

Bonus Resources:

1. An End-to-End AutoML Solution for Tabular Data at KaggleDays: https://ai.googleblog.com/2019/05/an-end-to-end-automl-solution-for.html

2. Hyperparameter tuning: https://en.wikipedia.org/wiki/Hyperparameter_optimization

3. Model selection: https://en.wikipedia.org/wiki/Model_selection

4. Neural architecture search: https://en.wikipedia.org/wiki/Neural_architecture_search

5. Neuroevolution: https://en.wikipedia.org/wiki/Neuroevolution

6. Self-tuning: https://en.wikipedia.org/wiki/Self-tuning

7. AutoML: Methods, Systems, Challenges (book): https://www.automl.org/book

8. AutoML for Keras: https://autokeras.com

Best Regards
Organizing team