hyperopt
optuna-examples
hyperopt | optuna-examples | |
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14 | 2 | |
7,086 | 599 | |
0.5% | 4.0% | |
5.3 | 8.7 | |
4 days ago | 3 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | MIT License |
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hyperopt
- Hyperopt: Distributed Asynchronous Hyper-Parameter Optimization
- Hyperopt: Distributed Hyperparameter Optimization
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[D]How to optimize an ANN?
You can use Optuna, SMAC or hyperopt
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How should one go about tuning hyper parameters?
Hyperopt - Distributed Asynchronous Hyperparameter Optimization in Python: https://github.com/hyperopt/hyperopt
- Hyperparameter tuning sklearn model using scripts and configs
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Finding the optimal parameter
Apart from the aforementioned comments noting that this is an optimization problem, ready-to-use python libraries for this kind of problem (accounting for evaluation time) include http://hyperopt.github.io/hyperopt/, https://github.com/automl/SMAC3, or https://www.ray.io/ray-tune
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Trading Algos - 5 Key Metrics and How to Implement Them in Python
Nothing can beat iteration and rapid optimization. Try running things like grid experiments, batch optimizations, and parameter searches. Take a look at various packages like hyperopt or optuna as packages that might be able to help you here!
- Discussion: the feasubility of using open source hyperparameter optimization tools and SQLAlchemy to automatically tune database performance
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How to automate hyperparameter tuning?
I suggest hyperopt
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How to use an optimizer in tensorflow 2.5?
Look into hyperopt they have a good documentation about optimization.
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
What are some alternatives?
optuna - A hyperparameter optimization framework
tqdm - :zap: A Fast, Extensible Progress Bar for Python and CLI
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Hyperactive - An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
SMAC3 - SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
pg_plan_advsr - PostgreSQL extension for automated execution plan tuning
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
StoRM - A neural network hyper parameter tuner
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.