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hyperparameter_tuning

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A collection of Hyper parameter tuning library.

// repository documentation

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# Hyper-parameter Tuning library

![CV logo](https://github.com/balavenkatesh3322/hyperparameter_tuning/blob/master/logo.jpg)

## What is Hyper-parameter Tuning?
Parameters which define the model architecture are referred to as hyperparameters and thus this process of searching for the ideal model architecture is referred to as hyperparameter tuning.

For instance,
How many trees should I include in my random forest?
How many neurons should I have in my neural network layer?
How many layers should I have in my neural network?

| Library Name | Description | Framework |
|   :---:      |     :---:      |     :---:     |
| [tpot]( https://github.com/EpistasisLab/tpot)  | TPOT stands for Tree-based Pipeline Optimization Tool. Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.   | `General`
| [nni]( https://github.com/microsoft/nni)  | NNI (Neural Network Intelligence) is a lightweight but powerful toolkit to help users automate Feature Engineering, Neural Architecture Search, Hyperparameter Tuning and Model Compression.   | `General`
| [xcessiv]( https://github.com/reiinakano/xcessiv)  | Xcessiv holds your hand through all the implementation details of creating and optimizing stacked ensembles so you're free to fully define only the things you care about.  | `General`
| [ray]( https://github.com/ray-project/ray)  | Ray is a fast and simple framework for building and running distributed applications.   | `General`
| [tune-sklearn]( https://github.com/ray-project/tune-sklearn)  | Tune-sklearn is a package that integrates Ray Tune's hyperparameter tuning and scikit-learn's models, allowing users to optimize hyerparameter searching for sklearn using Tune's schedulers.   | `General`
| [optuna]( https://github.com/optuna/optuna)  | Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning.   | `General`
| [Hyperopt]( https://github.com/hyperopt/hyperopt)  | Hyperopt is a Python library for serial and parallel optimization over awkward search spaces, which may include real-valued, discrete, and conditional dimensions.   | `General`
| [scikit-optimize]( https://github.com/scikit-optimize/scikit-optimize)  | Scikit-Optimize, or skopt, is a simple and efficient library to minimize (very) expensive and noisy black-box functions.   | `General`
| [Ax]( https://github.com/facebook/Ax)  |Ax is an accessible, general-purpose platform for understanding, managing, deploying, and automating adaptive experiments.   | `General`
| [Spearmint]( https://github.com/HIPS/Spearmint)  |Spearmint is a software package to perform Bayesian optimization.   | `General`
| [hyperparameter_hunter]( https://github.com/HunterMcGushion/hyperparameter_hunter)  | Automatically save and learn from Experiment results, leading to long-term, persistent optimization that remembers all your tests.   | `General`
| [sherpa]( https://github.com/sherpa-ai/sherpa)  |  A Python Hyperparameter Optimization Library   | `General`
| [auptimizer]( https://github.com/LGE-ARC-AdvancedAI/auptimizer)  | Auptimizer is an optimization tool for Machine Learning (ML) that automates many of the tedious parts of the model building process.   | `General`
| [advisor]( https://github.com/tobegit3hub/advisor)  | Advisor is the hyper parameters tuning system for black box optimization.   | `General`
| [test-tube](https://github.com/williamFalcon/test-tube ) | Test tube is a python library to track and parallelize hyperparameter search for Deep Learning and ML experiments.   | `General`
| [Determined]( https://github.com/determined-ai/determined)  | Determined helps deep learning teams train models more quickly, easily share GPU resources, and effectively collaborate.   | `General`

## Contributions
Your contributions are always welcome!!
Please have a look at `contributing.md`

## License

[MIT License](LICENSE)

hyperparameter_tuning

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