Topic: usg-artificial-intelligence Goto Github

👇 Here are 12 public repositories matching this topic...

cdcgov / legionella_pneumophila_genomics
usg-artificial-intelligence,This repository contains bioinformatics scripts and a Docker container to perform the in silico prediction of Legionella pneumophila serogroup from short read sequences using a supervised machine learning approach.
Organization: cdcgov
cdc docker-container legionella-pneumophila prediction usg-artificial-intelligence

cdcgov / nlpworkbench
usg-artificial-intelligence,Natural Language processing for Pathology reports on cancer histology, laterality, side, and behavior.
Organization: cdcgov
usg-artificial-intelligence usg-natural-language-processing

nasa / concept-tagging-api
usg-artificial-intelligence,Contains code for the API that takes in text and predicts concepts & keywords from a list of standardized NASA keywords. API is for exposing models created with the repository `concept-tagging-training`.
Organization: nasa
nlp nlp-machine-learning nasa nasa-api api flask-application usg-artificial-intelligence concept-tag machine-learning

nasa / concept-tagging-training
usg-artificial-intelligence,Contains code for training NLP models that takes in text and predicts concepts & keywords from a list of standardized NASA keywords. Code for the API that uses models trained by this repo is in `concept-tagging-api` repository.
Organization: nasa
nlp nlp-train nlp-machine-learning usg-artificial-intelligence concept-tagging makefile machine-learning

nasa / delta
usg-artificial-intelligence,Deep Learning for Satellite Imagery
Organization: nasa
deep-learning machine-learning remote-sensing usg-artificial-intelligence

nasa / ml-airport-arrival-runway
usg-artificial-intelligence,The ML-airport-arrival-runway software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended for predicting arrival runway assignments. The software is designed to point to databases which are not provided as part of the software release and thus this software is only intended to serve as an example of best practices. The software is built in python and leverages open-source libraries kedro, scikitlearn, MLFlow, and others. The software provides examples how to build three distinct pipelines for data query and save, data engineering, and data science. These pipelines enable scalable, repeatable, and maintainable development of ML models.
Organization: nasa
usg-artificial-intelligence airport airport-simulation machine-learning

nasa / ml-airport-configuration
usg-artificial-intelligence,The ML-airport-configuration software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended for predicting airport configuration as a time series. The software is designed to point to databases which are not provided as part of the software release and thus this software is only intended to serve as an example of best practices. The software is built in python and leverages open-source libraries kedro, scikitlearn, MLFlow, and others. The software provides examples how to build three distinct pipelines for data query and save, data engineering, and data science. These pipelines enable scalable, repeatable, and maintainable development of ML models.
Organization: nasa
usg-artificial-intelligence airport configuration usg-ai-training-data airport-simulation

nasa / ml-airport-departure-runway
usg-artificial-intelligence,The ML-airport-departure-runway software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended for predicting departure runway assignments. The software is designed to point to databases which are not provided as part of the software release and thus this software is only intended to serve as an example of best practices. The software is built in python and leverages open-source libraries kedro, scikitlearn, MLFlow, and others. The software provides examples how to build three distinct pipelines for data query and save, data engineering, and data science. These pipelines enable scalable, repeatable, and maintainable development of ML models.
Organization: nasa
usg-artificial-intelligence airport airport-simulation machine-learning

nasa / ml-airport-taxi-in
usg-artificial-intelligence,The ML-airport-taxi-in software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended for four distinct use cases: 1) unimpeded AMA taxi in, 2) unimpeded ramp taxi in, 3) impeded AMA taxi in, and 4) impeded ramp taxi in. The software is designed to point to databases which are not provided as part of the software release and thus this software is only intended to serve as an example of best practices. The software is built in python and leverages open-source libraries kedro, scikitlearn, MLFlow, and others. The software provides examples how to build three distinct pipelines for data query and save, data engineering, and data science. These pipelines enable scalable, repeatable, and maintainable development of ML models.
Organization: nasa
usg-artificial-intelligence airport machine-learning airport-simulation

nasa / ml-airport-taxi-out
usg-artificial-intelligence,The ML-airport-taxi-out software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended for four distinct use cases: 1) unimpeded AMA taxi out, 2) unimpeded ramp taxi out, 3) impeded AMA taxi out, and 4) impeded ramp taxi out. The software is designed to point to databases which are not provided as part of the software release and thus this software is only intended to serve as an example of best practices. The software is built in python and leverages open-source libraries kedro, scikitlearn, MLFlow, and others. The software provides examples how to build three distinct pipelines for data query and save, data engineering, and data science. These pipelines enable scalable, repeatable, and maintainable development of ML models.
Organization: nasa
usg-artificial-intelligence usg-ai-training-data machine-learning airport airport-simulation

nasa / pymkad
usg-artificial-intelligence,
Organization: nasa
usg-artificial-intelligence

nasa / reinforcement-learning-benchmarking
usg-artificial-intelligence,Scripts for running several OpenAI Baselines algorithms on all MuJoCo or Roboschool gym environments to compare performance.
Organization: nasa
ai ml machine-learning reinforcement-learning usg-artificial-intelligence