| h2o/h2o |
10,615 |
|
0 |
1 |
about 2 years ago |
1 |
February 27, 2018 |
673 |
mit |
C |
| H2O - the optimized HTTP/1, HTTP/2, HTTP/3 server |
| h2oai/h2o-3 |
7,487 |
|
62 |
33 |
3 days ago |
49 |
August 09, 2023 |
2,746 |
apache-2.0 |
Jupyter Notebook |
| H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc. |
| h2oai/wave |
3,789 |
|
0 |
5 |
about 2 years ago |
36 |
October 09, 2023 |
211 |
apache-2.0 |
Python |
| Realtime Web Apps and Dashboards for Python and R |
| h2oai/h2o-2 |
2,242 |
|
0 |
0 |
almost 6 years ago |
1 |
November 04, 2014 |
17 |
apache-2.0 |
Java |
| Please visit https://github.com/h2oai/h2o-3 for latest H2O |
| szilard/benchm-ml |
1,839 |
|
0 |
0 |
over 3 years ago |
0 |
|
11 |
mit |
R |
| A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.). |
| h2oai/sparkling-water |
957 |
|
0 |
6 |
over 2 years ago |
195 |
October 26, 2023 |
44 |
apache-2.0 |
Scala |
| Sparkling Water provides H2O functionality inside Spark cluster |
| benedekrozemberczki/awesome-gradient-boosting-papers |
930 |
|
0 |
0 |
almost 3 years ago |
0 |
|
1 |
cc0-1.0 |
Python |
| A curated list of gradient boosting research papers with implementations. |
| onnx/onnxmltools |
896 |
|
20 |
35 |
about 2 years ago |
22 |
March 01, 2023 |
121 |
apache-2.0 |
Python |
| ONNXMLTools enables conversion of models to ONNX |
| kaeyleo/jekyll-theme-H2O |
849 |
|
0 |
0 |
almost 6 years ago |
0 |
|
24 |
mit |
CSS |
| 🎉 A clean and delicate Jekyll theme. Jekyll博客主题 |
| kaiwaehner/kafka-streams-machine-learning-examples |
806 |
|
0 |
0 |
over 2 years ago |
0 |
|
10 |
apache-2.0 |
Java |
| This project contains examples which demonstrate how to deploy analytic models to mission-critical, scalable production environments leveraging Apache Kafka and its Streams API. Models are built with Python, H2O, TensorFlow, Keras, DeepLearning4 and other technologies. |
