GitHunt
JI

jimfleming/differentiation

Implementing (parts of) TensorFlow (almost) from Scratch

Implementing (parts of) TensorFlow (almost) from Scratch

A Walkthrough of Symbolic Differentiation

This literate programming
exercise will construct a simple 2-layer feed-forward neural network to compute
the exclusive or, using symbolic
differentiation
to
compute the gradients automatically. In total, about 500 lines of code,
including comments. The only functional dependency is numpy. I highly recommend
reading Chris Olah's Calculus on Computational Graphs:
Backpropagation
for more
background on what this code is doing.

Languages

Python100.0%

Contributors

Created October 13, 2016
Updated January 23, 2025
jimfleming/differentiation | GitHunt