sympyle

Simple Automatic Differentiation in Python


Keywords
backpropagation, computational-graphs, deep-learning, learning, machine-learning, neural-network, tutorial
License
GPL-3.0
Install
pip install sympyle==0.0.1.dev0

Documentation

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 and pytorch.

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 works on arbitrary 'networks'.

Examples and tutorials

All tutorials are under docs/source/tutorials and interactible at https://harveyslash.github.io/sympyle/ (under the tutorials section)