nasa/delta

Deep Learning for Satellite Imagery

Language: Python - Size: 9.88 MB - Last synced at: about 1 month ago - Pushed at: almost 2 years ago - Stars: 213 - Forks: 67

nasa/ML-airport-taxi-out

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.

Language: Python - Size: 12.9 MB - Last synced at: 2 months ago - Pushed at: over 3 years ago - Stars: 37 - Forks: 7

nasa/pymdptoolbox Fork of sawcordwell/pymdptoolbox

Markov Decision Process (MDP) Toolbox for Python

Language: Python - Size: 455 KB - Last synced at: about 1 year ago - Pushed at: about 10 years ago - Stars: 31 - Forks: 31

nasa/ML-airport-configuration

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.

Language: Python - Size: 9.63 MB - Last synced at: about 1 year ago - Pushed at: over 3 years ago - Stars: 24 - Forks: 3

nasa/concept-tagging-api

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`.

Language: Python - Size: 184 KB - Last synced at: about 1 year ago - Pushed at: over 1 year ago - Stars: 18 - Forks: 10

nasa/concept-tagging-training

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.

Language: Python - Size: 235 KB - Last synced at: about 1 year ago - Pushed at: over 2 years ago - Stars: 17 - Forks: 10

nasa/PyMKAD

Language: Python - Size: 1.04 MB - Last synced at: about 1 year ago - Pushed at: over 3 years ago - Stars: 9 - Forks: 4

nasa/Reinforcement-Learning-Benchmarking

Scripts for running several OpenAI Baselines algorithms on all MuJoCo or Roboschool gym environments to compare performance.

Language: Shell - Size: 15.6 KB - Last synced at: about 1 year ago - Pushed at: over 5 years ago - Stars: 9 - Forks: 3

CDCgov/NLPWorkbench

Natural Language processing for Pathology reports on cancer histology, laterality, side, and behavior.

Language: HTML - Size: 647 MB - Last synced at: 3 months ago - Pushed at: over 6 years ago - Stars: 9 - Forks: 12

nasa/ML-airport-arrival-runway

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.

Language: Python - Size: 3.57 MB - Last synced at: about 1 year ago - Pushed at: over 3 years ago - Stars: 7 - Forks: 0

nasa/ML-airport-departure-runway

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.

Language: Python - Size: 9.67 MB - Last synced at: about 1 year ago - Pushed at: over 3 years ago - Stars: 6 - Forks: 2

cdcai/premier_analysis

A deep learning project predicting hyperinflammatory syndrome among COVID-19 patients using EHR data.

Language: Jupyter Notebook - Size: 23.3 MB - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 5 - Forks: 11

nasa/ML-airport-taxi-in

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.

Language: Python - Size: 9.69 MB - Last synced at: about 1 year ago - Pushed at: over 3 years ago - Stars: 4 - Forks: 1

CDCgov/legionella_pneumophila_genomics 📦

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.

Language: Shell - Size: 2.09 MB - Last synced at: about 2 years ago - Pushed at: over 5 years ago - Stars: 3 - Forks: 5

cdcai/R-tensorflow-projects Fork of beansrowning/R-tensorflow-projects

Random examples of Tensorflow in R

Language: R - Size: 36.1 KB - Last synced at: about 2 years ago - Pushed at: about 5 years ago - Stars: 1 - Forks: 1

cdcai/autism_surveillance Fork of scotthlee/autism-surveillance 📦

Text classification algorithms for autism surveillance

Language: Python - Size: 35.2 KB - Last synced at: about 2 years ago - Pushed at: about 5 years ago - Stars: 0 - Forks: 1

cdcai/NRC Fork of scotthlee/NRC

Natural language generation for discrete data in EHRs

Language: Python - Size: 43 KB - Last synced at: about 2 years ago - Pushed at: about 5 years ago - Stars: 0 - Forks: 1

cdcai/enriched-LSTMs Fork of scotthlee/enriched-LSTMs

Classifying multimodal health data with LSTMs

Language: Python - Size: 53.7 KB - Last synced at: about 2 years ago - Pushed at: about 5 years ago - Stars: 0 - Forks: 1

cdcai/injury-autocoding Fork of scotthlee/injury-autocoding

An ensemble of BERTs for classifying injury narratives

Language: Python - Size: 168 KB - Last synced at: about 2 years ago - Pushed at: about 5 years ago - Stars: 0 - Forks: 1