A collection of jupyter notebooks for workshops on image classification and recognition using deep transfer learning
Written by Dr Daniel Buscombe Northern Arizona University daniel.buscombe@nau.edu
This workshop was prepared for the "MAPPING LAND-USE, HAZARD VULNERABILITY AND HABITAT SUITABILITY USING DEEP NEURAL NETWORKS" project, funded by the U.S. Geological Survey Community for Data Integration, 2018
Thanks: Jenna Brown, Paul Grams, Leslie Hsu, Andy Ritchie, Chris Sherwood, Rich Signell, Jon Warrick
These materials and instructions are currently for workshop participants only, and will be updated in October for general use
Many of these materials are mirrored in the dl_tools library
git clone https://github.com/dbuscombe-usgs/cdi_dl_workshop.git
cd cdi_dl_workshop
conda env create -f binder\environment.yml
conda activate cdi_workshop
python -m ipykernel install --user --name cdi_workshop --display-name "Python (cdi)"
jupyter notebook
<img width="960"
Go to http://pangeo.esipfed.org and log in with your github credentials. Note, this will only work if you are a member of the cdi-workshops github group (invitation only)
When your server starts up, you should see a jupyter in traditional 'tree' view
You can work in newer 'lab' view instead by modifying the url from 'tree' to 'lab'
Clone YOUR forked repository from YOUR github page
