Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Nni | 13,725 | 8 | 27 | 2 months ago | 55 | September 14, 2023 | 342 | mit | Python | |
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning. | ||||||||||
Vizier | 1,142 | 3 | 3 months ago | 35 | November 30, 2023 | 44 | apache-2.0 | Python | ||
Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service. | ||||||||||
Hyperparameter Optimization Of Machine Learning Algorithms | 1,025 | 2 years ago | mit | Jupyter Notebook | ||||||
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear) | ||||||||||
Hyperactive | 475 | 5 | 5 months ago | 75 | October 24, 2023 | 8 | mit | Python | ||
An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models. | ||||||||||
Snake Ga | 360 | 3 years ago | 9 | Python | ||||||
AI Agent that learns how to play Snake with Deep Q-Learning | ||||||||||
Sherpa | 294 | 1 | 3 | 4 years ago | 8 | November 23, 2019 | 13 | other | JavaScript | |
Hyperparameter optimization that enables researchers to experiment, visualize, and scale quickly. | ||||||||||
Lstm_anomaly_thesis | 191 | 3 years ago | 1 | mit | Jupyter Notebook | |||||
Anomaly detection for temporal data using LSTMs | ||||||||||
Learn To Select Data | 131 | 6 years ago | 1 | Python | ||||||
Code for Learning to select data for transfer learning with Bayesian Optimization | ||||||||||
Flexs | 112 | a year ago | 9 | January 21, 2021 | 31 | apache-2.0 | Jupyter Notebook | |||
Fitness landscape exploration sandbox for biological sequence design. | ||||||||||
Mlviz | 97 | 2 years ago | 2 | mit | Jupyter Notebook | |||||
Visualizations of machine learning models and algorithms |