HyperTune is a Python-based project focused on optimizing hyperparameters for machine learning and deep learning algorithms using nature-inspired optimization techniques. Hyperparameter optimization plays a crucial role in maximizing the performance and efficiency of machine learning and deep learning models. HyperTune aims to automate this process by leveraging genetic algorithms (GA) to efficiently explore the hyperparameter space. By intelligently searching for the best combination of hyperparameters, HyperTune helps researchers and practitioners achieve optimal model performance without the need for manual tuning.
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Hyperparameter Optimization: HyperTune provides support for optimizing hyperparameters of various algorithms, including Random Forest, CatBoost, Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN). It utilizes genetic algorithms to efficiently search the hyperparameter space and find the best values for optimal model performance.
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Implementation of Genetic Algorithms: HyperTune incorporates genetic algorithm for hyperparameter tuning. This nature-inspired optimization technique enables HyperTune to intelligently explore the hyperparameter space and efficiently converge towards optimal solutions.
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Integration with Popular Libraries: HyperTune integrates with popular machine learning and deep learning libraries such as scikit-learn and TensorFlow. This allows users to leverage the power of these libraries while benefiting from HyperTune's hyperparameter optimization capabilities.
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Customizable for Different Datasets: HyperTune is designed to be easily customizable for different datasets and optimization goals. It provides flexibility in defining the search space for hyperparameters, allowing users to tailor the optimization process according to their specific requirements.
To install HyperTune, simply clone the repository:
git clone [email protected]:nabdullaev/HyperTune.git
cd HyperTune
HyperTune is the result of collaboration between multiple contributors. We would like to acknowledge the following individuals for their valuable contributions to the project:
- Ruslan Izmailov - Genetic Algorithm Implementation, CNN and ANN hyperparameter tuning - GitHub
- Nursultan Abdullaev - Custom Genetic Optimizer for CNN - GitHub
- Anna Gromova - CatBoost and Random Forest hyperparameter tuning - GitHub
HyperTune is released under the MIT License. Feel free to use, modify, and distribute this project according to the terms of the license.