Metadata-Version: 2.1
Name: sympyle
Version: 0.0.12
Summary: Simple Automatic Differentiation in Python 
Home-page: https://github.com/harveyslash/sympyle
Author: Harshvardhan Gupta
Author-email: theharshvardhangupta@gmail.com
License: GNU GENERAL PUBLIC LICENSE
Platform: UNKNOWN
Description-Content-Type: text/markdown
Requires-Dist: numpy

# Sympyle 
Simple Symbolic Graphs in Python


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## About

##### Project documentation: http://harveyslash.github.io/sympyle/


Sympyle is a Python library to demonstrate the inner workings of Computational
Graphs. Computational Graphs are used by highly optimised computational
frameworks like [tensorflow](https://tensorflow.org) and
[pytorch](https://pytorch.org).

However, these frameworks make several assumptions and optimisations in order
to optimise for speed and memory. This often makes it harder to understand
the inner workings of how these libraries work.

Sympyle is a simplified model library to demonstrate the working of
computational graphs, and how
[backpropagation](https://en.wikipedia.org/wiki/Backpropagation)
works on arbitrary 'networks'.

### Examples and tutorials coming soon

For now , you can see tests/ folder for usage 


