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h2oai / h2o-3

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Open Source Fast Scalable Machine Learning Platform For Smarter Applications: Deep Learning, Gradient Boosting & XGBoost, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

python java data-science machine-learning multi-threading opensource r big-data spark deep-learning hadoop random-forest gpu naive-bayes h2o distributed pca gbm ensemble-learning automl
  • Updated Oct 6, 2020
  • Jupyter Notebook

rapidsai / cudf

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kaiwaehner / kafka-streams-machine-learning-examples

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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.

python java open-source machine-learning kafka deep-learning tensorflow h2o keras kafka-client kafka-streams dl4j keras-tensorflow h2oai deeplearning4j ksql
  • Updated Apr 23, 2020
  • Java
awesome-gradient-boosting-papers

benedekrozemberczki / awesome-gradient-boosting-papers

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A curated list of gradient boosting research papers with implementations.

classifier machine-learning deep-learning random-forest h2o xgboost lightgbm gradient-boosting-machine adaboost decision-tree gradient-boosting-classifier classification-algorithm gradient-boosting boosting classification-trees xgboost-algorithm catboost gradient-boosted-trees classification-tree gradient-boosting-decision-trees
  • Updated Aug 2, 2020
  • Python

jphall663 / interpretable_machine_learning_with_python

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Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

python data-science machine-learning data-mining h2o gradient-boosting-machine transparency decision-tree fairness lime accountability interpretability interpretable-ai interpretable-ml xai fatml interpretable interpretable-machine-learning iml machine-learning-interpretability
  • Updated Oct 1, 2020
  • Jupyter Notebook
RemixAutoML

AdrianAntico / RemixAutoML

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R package for automation of machine learning, forecasting, feature engineering, model evaluation, model interpretation, data generation, and recommenders.

machine-learning r timeseries transformations h2o regression supervised-learning xgboost classification gpu-acceleration recommender-system feature-engineering unsupervised-learning multiclass-classification automated-machine-learning anomaly-detection market-basket-analysis catboost nonlinear-regression cost-sensitive-classification
  • Updated Oct 1, 2020
  • R

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