A perceptron is the simplest form of 'neural network' learning.
The 'neuron' is simply a vector that can be multiplied against data points to say 1 or 0.
This perceptron tries to learn which letters are vowels, ie, we draw 26 points in space and say some are one class and the rest are another.
- select what letter (or groups of letters) to feed to the perceptron next
- watch it's learning pattern (initially, moving left after consonants, right after vowels)
- see the weight vector at bottom of page
- see the current accuracy as compared to a benchmark accuracy from a linear algebra class separation solution (LDA)
- adjust the dimensionality
Requires numpy/scipy (if you need these look up anaconda distribution)
Install python dependencies with
pip install .
from base directory.
Build JS app from
npm install npm run build
/voweler/dist/ should exist
python wsgi.py to run app simply.
Cross-request state persistence is available via shared memory managed by
uwsgi: see the multiapp branch