Metadata-Version: 2.0
Name: sng
Version: 0.3.1
Summary: Generate name proposals for companies, software, etc.
Home-page: http://github.com/AlexEngelhardt/startup-name-generator
Author: Alexander Engelhardt
Author-email: alexander.w.engelhardt@gmail.com
License: MIT
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Text Processing :: Linguistic
Classifier: Topic :: Utilities
Requires-Dist: pyyaml
Requires-Dist: keras
Requires-Dist: tensorflow
Requires-Dist: numpy

startup-name-generator
======================

- `This package on PyPI <https://pypi.org/project/sng/>`_
- `This package on GitHub <https://github.com/AlexEngelhardt/startup-name-generator>`_

Summary
-------

This package can train a model that learns the "structure" of the words in a
supplied text corpus. It then generates new words with a similar structure,
which can be used as suggestions for naming things like companies or software.

Quickstart
----------

Check out the `Jupyter Notebook(s) in doc/notebooks/ <https://startup-name-generator.readthedocs.io/en/latest/notebooks/01_quickstart.html>`_.

Documentation
-------------

- The full documentation is `available online <https://startup-name-generator.readthedocs.io/en/latest/>`_
- I also gave a lightning talk presenting the basic idea, it's available `on Youtube <https://www.youtube.com/watch?v=1w3Q3CEldG0>`_.

Extended summary
----------------

Naming a startup is `hard <https://mashable.com/2012/10/04/startup-naming/>`_.

I therefore wrote a Python package to randomly generate company name ideas.

It takes an arbitrary text as input, and then trains a recurrent neural network
(RNN) on each its words, learning the structure of the text. The input text can
be a simple word list (e.g. Greek or Gallic), or a chapter from a book, or just
a random list of words (e.g. all Pokemon). The script then generates new random
names that sound simliar to the provided list.

Literature/References
---------------------

- `Andrew Ng's Deep Learning MOOC <https://www.deeplearning.ai/>`_
- http://karpathy.github.io/2015/05/21/rnn-effectiveness/
- https://github.com/keras-team/keras/blob/master/examples/lstm_text_generation.py



