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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 Jun 15, 2020
  • Jupyter Notebook

rapidsai / cudf

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Open

[FEA] `to_arrow` should clear the padding bits in the bitmask

1
jrhemstad
jrhemstad commented Jul 10, 2019

Is your feature request related to a problem? Please describe.
According to the Arrow spec:

Bitmaps are to be initialized to be all unset at allocation time (this includes padding).

This would imply that bits outside the range [0, size) should always be zero. However, in cuDF/libcudf, we take a more conservative approach and say that bits outside [0,size) are undefined in order to a

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cuDF (Python) feature request good first issue libcudf
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h2oai / h2o-tutorials

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Intro H2o tutorial not works

zdong1
zdong1 commented Jun 21, 2018

In tutorial h2o-tutorials/h2o-open-tour-2016/chicago/intro-to-h2o.R:
When I run data <- h2o.importFile(loan_csv), it would not import the data, instead, it returns:
https://raw.githubusercontent.com/h2oai/app-consumer-loan/master/data/loan.csv failed to importError in h2o.importFolder(path, pattern = "", destination_frame = destination_frame, : all files failed to import

I am using

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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 Jun 14, 2020
  • Python

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

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 Jun 4, 2020
  • Jupyter Notebook

h2oai / mli-resources

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Examples via Docker: missing mli-resources clone & jupyter to be run w/ --allow-root

5
dvorka
dvorka commented Sep 21, 2018

In order to successfully install examples using Docker I did the following changes:

  • There seems to be missing step which clones mli-resources GitHub repository. Perhaps RUN git clone https://github.com/h2oai/mli-resources.git should be added to Dockerfile (I cloned repo manually).
  • Jupyter refuses to start under root - consider adding --allow-root parameter: `docker run -i -t -p 888
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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 Jun 12, 2020
  • R

mercury-ml-team / mercury-ml

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Open

Develop TensorFlow serving implementations

karlschriek
karlschriek commented Feb 21, 2019

TensorFlow Serving client-side capabilities are currently not very clearly documented. We should aim to create an implementation that simplifies interacting with the REST or gRPC APIs for actions such as get_model_status, get_model_meta_data, hot_reload_model_config, predict and so forth.

This should go under mercury_ml.tensorflow.serving.

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enhancement

tgsmith61591 / skutil

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Open

Mark the docs page "No longer supported" to point folk in the right direction?

ianozsvald
ianozsvald commented Aug 16, 2018

Hi Tom. I had skutil tagged for a while to try your SafeLabelEncoder. I finally got to try to install it today and after a few minutes tracking dependencies backwards in conda (getting openjdk installed for h20), I jumped to your github page and saw that this module was deprecated.

Can I suggest you added a headline note at https://www.alkaline-ml.com/skutil/index.html saying the same, and

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