HyperGBM
AutoML pipeline tool
Automated machine learning tool for tabular data pipelines
A full pipeline AutoML tool for tabular data
343 stars
16 watching
46 forks
Language: Python
last commit: 8 months ago adversarial-validationautomlcatboostdaskdask-distributeddatacleaningdistributed-trainingensemble-learningfullpipelinegbmgpu-accelerationlightgbmpreprocessingpseudo-labelingrapidsaisemi-supervised-learningsklearntabular-dataxgboost
Related projects:
Repository | Description | Stars |
---|---|---|
| An autoML tool that searches and optimizes neural network architectures using tensorflow and keras. | 30 |
| Automates model building and deployment process by optimizing hyperparameters and compressing models for edge computing. | 200 |
| Automated machine learning with tree search optimization | 16 |
| An automated machine learning framework that simplifies the development of end-to-end AutoML toolkits in specific domains. | 267 |
| An optimization framework for machine learning hyperparameters | 1,093 |
| A reinforcement learning-based framework for optimizing hyperparameters in distributed machine learning environments. | 15 |
| A toolset for optimizing hyperparameters of machine learning models using Bayesian optimization and real-time visualization. | 136 |
| An algorithm for training self-generalizing gradient boosting machines with automatic hyperparameter optimization and improved performance on various machine learning tasks | 321 |
| A distributed framework for optimizing hyperparameters in machine learning models | 612 |
| Automated machine learning system for selecting promising models or pipelines for new datasets | 82 |
| A collection of benchmark problems for hyperparameter optimization | 140 |
| Accelerates machine learning algorithms on GPUs to improve performance and efficiency | 695 |
| An automated machine learning framework that generates optimal machine learning pipelines for various real-world problems. | 649 |
| A Python package for gradient-based function optimization in machine learning | 181 |
| An open-source project providing hardware accelerated, batchable and differentiable optimizers in JAX for deep learning. | 941 |