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* remove redundant doc and add tutorial * add demos for pydata2023 * Update pydata23 docs * remove redundant notebooks * Move tutorial notebooks to notebook folder * update readme and notebook links * update notebook links * update links * update readme --------- Co-authored-by: Li Jiang <lijiang1@microsoft.com> Co-authored-by: Li Jiang <bnujli@gmail.com>
68 lines
4.9 KiB
Markdown
68 lines
4.9 KiB
Markdown
# AAAI 2023 Lab Forum - LSHP2: Automated Machine Learning & Tuning with FLAML
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## Session Information
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**Date and Time**: February 8, 2023 at 2-6pm ET.
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Location: Walter E. Washington Convention Center, Washington DC, USA
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Duration: 4 hours (3.5 hours + 0.5 hour break)
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For the most up-to-date information, see the [AAAI'23 Program Agenda](https://aaai.org/Conferences/AAAI-23/aaai23tutorials/)
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## [Lab Forum Slides](https://1drv.ms/b/s!Ao3suATqM7n7iokCQbF7jUUYwOqGqQ?e=cMnilV)
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## What Will You Learn?
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- What FLAML is and how to use FLAML to
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- find accurate ML models with low computational resources for common ML tasks
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- tune hyperparameters generically
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- How to leverage the flexible and rich customization choices
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- finish the last mile for deployment
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- create new applications
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- Code examples, demos, use cases
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- Research & development opportunities
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## Session Agenda
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### **Part 1. Overview of FLAML**
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- Overview of AutoML and FLAML
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- Basic usages of FLAML
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- Task-oriented AutoML
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- [Documentation](https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML)
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- [Notebook: A classification task with AutoML](https://github.com/microsoft/FLAML/blob/tutorial-aaai23/notebook/automl_classification.ipynb); [Open In Colab](https://colab.research.google.com/github/microsoft/FLAML/blob/tutorial-aaai23/notebook/automl_classification.ipynb)
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- Tune User-Defined-functions with FLAML
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- [Documentation](https://microsoft.github.io/FLAML/docs/Use-Cases/Tune-User-Defined-Function)
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- [Notebook: Tune user-defined function](https://github.com/microsoft/FLAML/blob/tutorial-aaai23/notebook/tune_demo.ipynb); [Open In Colab](https://colab.research.google.com/github/microsoft/FLAML/blob/tutorial-aaai23/notebook/tune_demo.ipynb)
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- Zero-shot AutoML
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- [Documentation](https://microsoft.github.io/FLAML/docs/Use-Cases/Zero-Shot-AutoML)
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- [Notebook: Zeroshot AutoML](https://github.com/microsoft/FLAML/blob/tutorial-aaai23/notebook/zeroshot_lightgbm.ipynb); [Open In Colab](https://colab.research.google.com/github/microsoft/FLAML/blob/tutorial-aaai23/notebook/zeroshot_lightgbm.ipynb)
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- [ML.NET demo](https://learn.microsoft.com/dotnet/machine-learning/tutorials/predict-prices-with-model-builder)
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Break (15m)
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### **Part 2. Deep Dive into FLAML**
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- The Science Behind FLAML’s Success
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- [Economical hyperparameter optimization methods in FLAML](https://microsoft.github.io/FLAML/docs/Use-Cases/Tune-User-Defined-Function/#hyperparameter-optimization-algorithm)
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- [Other research in FLAML](https://microsoft.github.io/FLAML/docs/Research)
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- Maximize the Power of FLAML through Customization and Advanced Functionalities
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- [Notebook: Customize your AutoML with FLAML](https://github.com/microsoft/FLAML/blob/tutorial-aaai23/notebook/customize_your_automl_with_flaml.ipynb); [Open In Colab](https://colab.research.google.com/github/microsoft/FLAML/blob/tutorial-aaai23/notebook/customize_your_automl_with_flaml.ipynb)
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- [Notebook: Further acceleration of AutoML with FLAML](https://github.com/microsoft/FLAML/blob/tutorial-aaai23/notebook/further_acceleration_of_automl_with_flaml.ipynb); [Open In Colab](https://colab.research.google.com/github/microsoft/FLAML/blob/tutorial-aaai23/notebook/further_acceleration_of_automl_with_flaml.ipynb)
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- [Notebook: Neural network model tuning with FLAML ](https://github.com/microsoft/FLAML/blob/tutorial-aaai23/notebook/tune_pytorch.ipynb); [Open In Colab](https://colab.research.google.com/github/microsoft/FLAML/blob/tutorial-aaai23/notebook/tune_pytorch.ipynb)
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### **Part 3. New features in FLAML**
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- Natural language processing
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- [Notebook: AutoML for NLP tasks](https://github.com/microsoft/FLAML/blob/tutorial-aaai23/notebook/automl_nlp.ipynb); [Open In Colab](https://colab.research.google.com/github/microsoft/FLAML/blob/tutorial-aaai23/notebook/automl_nlp.ipynb)
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- Time Series Forecasting
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- [Notebook: AutoML for Time Series Forecast tasks](https://github.com/microsoft/FLAML/blob/tutorial-aaai23/notebook/automl_time_series_forecast.ipynb); [Open In Colab](https://colab.research.google.com/github/microsoft/FLAML/blob/tutorial-aaai23/notebook/automl_time_series_forecast.ipynb)
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- Targeted Hyperparameter Optimization With Lexicographic Objectives
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- [Documentation](https://microsoft.github.io/FLAML/docs/Use-Cases/Tune-User-Defined-Function/#lexicographic-objectives)
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- [Notebook: Find accurate and fast neural networks with lexicographic objectives](https://github.com/microsoft/FLAML/blob/tutorial-aaai23/notebook/tune_lexicographic.ipynb); [Open In Colab](https://colab.research.google.com/github/microsoft/FLAML/blob/tutorial-aaai23/notebook/tune_lexicographic.ipynb)
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- Online AutoML
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- [Notebook: Online AutoML with Vowpal Wabbit](https://github.com/microsoft/FLAML/blob/tutorial-aaai23/notebook/autovw.ipynb); [Open In Colab](https://colab.research.google.com/github/microsoft/FLAML/blob/tutorial-aaai23/notebook/autovw.ipynb)
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- Fair AutoML
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### Challenges and open problems
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