/randomized-SVD

demos for PyBay talk: Using Randomness to make code faster

Primary LanguageJupyter Notebook

This is the demo for my PyBay 2017 talk. If you are not comfortable setting up the demo or unable to do so, you will still get a lot out of the session.

I recommend downloading Anaconda, which contains the main Python scientific libraries. Alternately, you can create a virtual environment and install the necessary requirements:

virtualenv env
source env/bin/activate
pip install -r requirements.txt

To start the jupyter notebook from the command line:

jupyter notebook

Data Sources

Part 1: Word Embeddings

The dataset of word embeddings is available at http://files.fast.ai/models/glove/6B.100d.tgz To download and unzip the files from the command line, you can run:

wget http://files.fast.ai/models/glove_50_glove_100.tgz 
tar xvzf glove_50_glove_100.tgz

Part 2: Background Removal

Download the real video 003 and 008 datasets from BMC 2012 Background Models Challenge Dataset