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        "author": "Harshvardhan Gupta",
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        "description": "# Sympyle \nSimple Symbolic Graphs in Python\n\n\n[![Build Status](https://travis-ci.com/harveyslash/sympyle.svg?branch=master)](https://travis-ci.com/harveyslash/sympyle)\n[![codecov](https://codecov.io/gh/harveyslash/Sympyle/branch/master/graph/badge.svg)](https://codecov.io/gh/harveyslash/Sympyle)\n[![CodeFactor](https://www.codefactor.io/repository/github/harveyslash/sympyle/badge/master)](https://www.codefactor.io/repository/github/harveyslash/sympyle/overview/master)\n\n\n## About\n\n##### Project documentation: http://harveyslash.github.io/sympyle/\n\n\nSympyle is a Python library to demonstrate the inner workings of Computational\nGraphs. Computational Graphs are used by highly optimised computational\nframeworks like [tensorflow](https://tensorflow.org) and\n[pytorch](https://pytorch.org).\n\nHowever, these frameworks make several assumptions and optimisations in order\nto optimise for speed and memory. This often makes it harder to understand\nthe inner workings of how these libraries work.\n\nSympyle is a simplified model library to demonstrate the working of\ncomputational graphs, and how\n[backpropagation](https://en.wikipedia.org/wiki/Backpropagation)\nworks on arbitrary 'networks'.\n\n### Examples and tutorials coming soon\n\nFor now , you can see tests/ folder for usage \n\n\n",
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