BotLibre
optuna
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BotLibre | optuna | |
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1 | 32 | |
565 | 9,471 | |
1.4% | 3.0% | |
7.2 | 9.9 | |
3 months ago | 2 days ago | |
Java | Python | |
Eclipse Public License 1.0 | GNU General Public License v3.0 or later |
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BotLibre
optuna
- How to test optimal parameters
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How did you make that?!
The network configuration process is usually not particularly scientific and mostly relies on empirical observation. For some cases, tools like Optuna can be used to automatically find the optimal parameters. In others, on others, you can look for modern studies which explore the effect of this parameter on performance, such as this study (2022), but these are typically very specific to one particular architecture.
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[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
Keras Tuner, Optuna : https://github.com/optuna/optuna ?
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Suggestion to optimize algo
I have used OpenTuner, but I don't think it is maintained anymore. I hear tell that Optuna is what to use now, but have not used it myself. https://optuna.org Optuna - A hyperparameter optimization framework
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[D]How to optimize an ANN?
You can use Optuna, SMAC or hyperopt
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Optuna: An open source hyperparameter optimization framework to automate hyperparameter search
Optuna is a great library and I do use it in tuneta for optimizing technical indicator parameters. However, certain Optuna algos suggest the same parameters in separate trials resulting in many duplicate parameters (issue) which needs to be managed external of the lib.
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The loss function of my model (Not a NN model) is not differentiable, what should I do?
if your parameter set is not too large, you could try black-box optimization via something like Optuna
- SPO600 project part 1
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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!
What are some alternatives?
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.
hyperopt - Distributed Asynchronous Hyperparameter Optimization in Python
rl-baselines3-zoo - A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
pyGAM - [HELP REQUESTED] Generalized Additive Models in Python
pg_plan_advsr - PostgreSQL extension for automated execution plan tuning
SMAC3 - SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
Empirical_Study_of_Ensemble_Learning_Methods - Training ensemble machine learning classifiers, with flexible templates for repeated cross-validation and parameter tuning
highway - Performance-portable, length-agnostic SIMD with runtime dispatch
optuna-examples - Examples for https://github.com/optuna/optuna
xsimd - C++ wrappers for SIMD intrinsics and parallelized, optimized mathematical functions (SSE, AVX, AVX512, NEON, SVE))