nni
LightAutoML
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nni | LightAutoML | |
---|---|---|
5 | 1 | |
13,726 | 767 | |
0.9% | - | |
6.7 | 9.2 | |
about 2 months ago | about 2 years ago | |
Python | Python | |
MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
nni
- Filter Pruning for PyTorch
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Automated Machine Learning (AutoML) - 9 Different Ways with Microsoft AI
For a complete tutorial, navigate to this Jupyter Notebook: https://github.com/microsoft/nni/blob/master/examples/notebooks/tabular_data_classification_in_AML.ipynb
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[D] Efficient ways of choosing number of layers/neurons in a neural network
optuna, hyperopt, nni, plenty of less-known tools too.
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Top 10 Developer Trends, Sun Oct 18 2020
microsoft / nni
LightAutoML
What are some alternatives?
optuna - A hyperparameter optimization framework
FEDOT - Automated modeling and machine learning framework FEDOT
FLAML - A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
cookiecutter-data-science - A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
autogluon - AutoGluon: Fast and Accurate ML in 3 Lines of Code
jupyter - Jupyter metapackage for installation, docs and chat
AutoML - This is a collection of our NAS and Vision Transformer work. [Moved to: https://github.com/microsoft/Cream]
hyperopt - Distributed Asynchronous Hyperparameter Optimization in Python
lazypredict - Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning
archai - Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.
Language_Identifier - Language Identification classification using XGBoost