Intro

This repo contains code developed for the CDA class - Fall 2019

Setup steps

python3 -m venv .env
source ./.env/bin/activate
pip install -r requirements.txt

Image processing

./parse_images.py -h
usage: parse_images.py [-h] [-i INPUTDIR] [-o OUTPUTDIR] [-w WIDTH]

optional arguments:
  -h, --help            show this help message and exit
  -i INPUTDIR, --inputdir INPUTDIR
                        input directory name
  -o OUTPUTDIR, --outputdir OUTPUTDIR
                        output directory name
  -w WIDTH, --width WIDTH
                        output image width (default 512 px)

References

Anaconda

brew cask install anaconda
du -hcs /usr/local/Caskroom/anaconda/2019.10/Anaconda3-2019.10-MacOSX-x86_64.sh
    ==> 424M

Update path to make accessible - add to ~/.zshrc
    export PATH="/usr/local/anaconda3/bin:$PATH"
 source ~/.zshrc

conda init zsh
conda create -n test-conda
conda activate test-conda
jupyter notebook

conda deactivate
conda remove -n test-conda -all

Geohash

• https://en.wikipedia.org/wiki/Geohash#Design
• http://geohash.org/djm2wjk4u0rm
• https://github.com/vinsci/geohash/blob/master/Geohash/geohash.py

Picking the number of clusters

• (!) https://github.com/google/open-location-code/blob/master/python/openlocationcode_test.py
• (!) https://stackoverflow.com/questions/44416764/loading-folders-of-images-in-tensorflow
• (!) https://datascience.stackexchange.com/questions/761/clustering-geo-location-coordinates-lat-long-pairs/25424#25424
• https://nbviewer.jupyter.org/github/nborwankar/LearnDataScience/blob/master/notebooks/D3.%20K-Means%20Clustering%20Analysis.ipynb
• https://www.exiv2.org/tags.html

Deep Learning: Our Miraculous Year 1990-1991

• http://people.idsia.ch/~juergen/deep-learning-miraculous-year-1990-1991.html