Skip to content

deepxiangfa/differentiation

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

43 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

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.

About

Implementing (parts of) TensorFlow (almost) from Scratch

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

 
 
 

Contributors

Languages

  • Python 100.0%