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H2O.ai

Login: h2oai

Company: null

Location: Mountain View, CA

email: h2ostream@googlegroups.com

Blog: http://h2o.ai

Members

Repositories

2017-06-21-hackathon: Meetup Hackathon 06/21/2017
aggregator-zoom: null
app-ask-craig: Ask Craig application
app-consumer-loan: null
app-malicious-domains: Domain name classifier looking for good vs. possibly malicious providers
app-mojo-servlet: Example of putting a mojo zip file as a resource into a java servlet.
app-news-classification: null
app-titanic: null
awesome-h2o: A curated list of research, applications and projects built using H2O Machine Learning
bot-test: null
caffe: Caffe: a fast open framework for deep learning.
correlation-graph: null
CSC-HCI-480-2013-repo: null
cub: CUB is a flexible library of cooperative threadblock primitives and other utilities for CUDA kernel programming.
Cyclops: Cyclops (Cyclic coordinate descent for logistic, Poisson and survival analysis) is an R package for performing large scale regularized regressions.
daal: Intel� Data Analytics Acceleration Library (Intel� DAAL)
dai-centos7-ppc64le-cuda8-nae: null
dai-centos7-ppc64le-nae: null
dai-centos7-x86_64-nae: null
dai-ubuntu16-ppc64le-nae: null
dai-ubuntu16-x86_64-nae: null
dallas-tutorials: Temporary repository for fast git cloning during the h2o dallas event.
data-science-examples: A collection of data science examples implemented across a variety of languages and libraries.
datatable: data.table for Python
db-benchmark: Speed Benchmark
Deep-Learning-with-h2o-in-R: Deep neural networks on over 50 classification problems from the UC Irvine Machine Learning Repository
deepwater: Deep Learning in H2O using Native GPU Backends
deepwater-nae: null
dmlc-core: A common bricks library for building scalable and portable distributed machine learning.
docker-python: Kaggle Python docker image
drift: an ES2015 port of h2o-flow
fastr: null
fluid: Rapid application development for a more... civilized age.
glmnet_python: null
google-analytics-java: Java API for Google Analytics Measurement Protocol (part of Universal Analytics).
h2o-2: h2o = fast statistical, machine learning & math runtime for bigdata
h2o-3: Open Source Fast Scalable Machine Learning Platform For Smarter Applications (Deep Learning, Gradient Boosting, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), ...)
h2o3-automl-nae: null
h2o3-nae: null
h2o3-power-nae: null
h2o3-sagemaker: Integrating H2O-3 AutoML with Amazon Sagemaker
h2o3-sagemaker-poc: null
h2o3-xgboost-nae: null
h2o4gpu: H2Oai GPU Edition
h2oai-kmeans: kmeans clustering with multi-GPU capabilities
h2oai-power-nae: null
h2o-cloud: H2O Cloud code.
h2odocs.old: h2odocs
h2o-droplets: Templates for projects based on top of H2O.
h2o-flow: Web based interactive computing environment for H2O
h2o-google-bigquery: null
h2o-jenkins-pipeline-lib: Library of different Jenkins pipeline building blocks.
h2o.js: Node.js bindings to H2O, the open-source prediction engine for big data science.
h2o-kubeflow: null
h2o-meetups: Presentations from H2O meetups & conferences by the H2O.ai team
h2o-rf1-bench: null
h2o-scoring-service: Scoring service backend by model POJOs.
h2o-sparkling: DEPRECATED! Use https://github.com/h2oai/sparkling-water repository! H2O and Spark interoperability based on Tachyon.
h2o-tutorials: Tutorials and training material for the H2O Machine Learning Platform
h2o-world-2014-training: training material
high-scale-lib: A fork of Cliff Click's High Scale Library. Improved with bug fixes and a real build system.
incubator-metron: Mirror of Apache Metron (Incubating)
jacocoHighlight: null
javacpp: The missing bridge between Java and native C++
javacpp-presets: The missing bridge between Java and native C++ libraries
jenkins-plugins: A simple radiator view for passed tests. See http://test.0xdata.com/
kmcuda: Large scale K-means and K-nn implementation on NVIDIA GPU / CUDA
mli-resources: Machine Learning Interpretability Resources
mojoland: Testing ground to ensure mojos backward compatibility
mshadow: Matrix Shadow:Lightweight CPU/GPU Matrix and Tensor Template Library in C++/CUDA for (Deep) Machine Learning
mxnet: Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
MXNet.cpp: CPP wrapper for MXNet interface
nccl: Optimized primitives for collective multi-GPU communication (with Windows / Visual Studio capability)
nnvm: Intermediate Computational Graph Representation for Deep Learning Systems
openml-python: Python module to interface with OpenML
perf: Performance Benchmarks
perrier: Perrier - A blend of Spark & H2O
ps-lite: A lightweight parameter server interface
py3nvml: Python 3 Bindings for NVML library. Get NVIDIA GPU status inside your program.
pydata2016-h2o-loganalysis: Log Analysis Use Case for PyData2016
qcon2015: Repository for SF QConf 2015 Workshop
redis: Redis is an in-memory database that persists on disk. The data model is key-value, but many different kind of values are supported: Strings, Lists, Sets, Sorted Sets, Hashes, HyperLogLogs, Bitmaps.
reflections: **This is a fork of reflections library to unblock our development with the newest version of this library. After official 0.9.11 release of the library, this repository should be deleted.**
residuals-vis: null
residuals-vis-data: null
residuals-vis-example-project: null
roc-chart: null
rsparkling: RSparkling: Use H2O Sparkling Water from R (Spark + R + Machine Learning)
scikit-learn: scikit-learn: machine learning in Python
social_ml: null
sparkling-water: Sparkling Water provides H2O functionality inside Spark cluster
steam: Build, manage and deploy H2O's high-speed machine learning models.
tensorflow: Computation using data flow graphs for scalable machine learning
typesentry: Python 2.7 & 3.5+ runtime type-checker
xgboost: Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow

This work is supported by the National Institutes of Health's National Center for Advancing Translational Sciences, Grant Number U24TR002306. This work is solely the responsibility of the creators and does not necessarily represent the official views of the National Institutes of Health.