optuna-examples
nni
optuna-examples | nni | |
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2 | 5 | |
601 | 13,777 | |
4.3% | 0.7% | |
8.7 | 4.5 | |
6 days ago | 5 days ago | |
Python | Python | |
MIT License | MIT License |
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optuna-examples
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[D]How to optimize an ANN?
Check out the examples for Optuna, a popular hyper parameter tuning package. It has examples for most popular ML frameworks including Xgboost, so you can see how it compares to an ANN framework like Keras or PyTorch.
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Data Scientists are dying out
That's still regular ML because you are in charge of the features. Optuna might make your life easier though: https://github.com/optuna/optuna-examples/blob/main/xgboost/xgboost_simple.py
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
What are some alternatives?
tqdm - :zap: A Fast, Extensible Progress Bar for Python and CLI
optuna - A hyperparameter optimization framework
Hyperactive - An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
FLAML - A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
autogluon - Fast and Accurate ML in 3 Lines of Code
hyperopt - Distributed Asynchronous Hyperparameter Optimization in Python
AutoML - This is a collection of our NAS and Vision Transformer work. [Moved to: https://github.com/microsoft/Cream]
SMAC3 - SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
archai - Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.