https://minitorch.github.io/

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  * Setup
  * ML Primer
  * Fundamentals
  * Autodiff
  * Tensors
  * Efficiency
  * Networks

  * GitHub
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On this page

  * module0
  * module1
  * module2
  * module3
  * module4
  * Slides
      + Indices and tables

_images/minitorch.svg


MiniTorch (https://github.com/minitorch/) is a diy teaching library
for machine learning engineers who wish to learn about the internal
concepts underlying deep learning systems. It is a pure Python
re-implementation of the Torch API designed to be simple,
easy-to-read, tested, and incremental. The final library can run
Torch code. The project was developed for the course Machine Learning
Engineering at Cornell Tech.

To get started, first read Setup and Contributing to build your
workspace. Then follow through each of the modules to the right.
Minimal computational resources are required. Module starting code is
available on GitHub, and each proceeds incrementally from past
modules.

Enjoy!

Sasha Rush (@srush_nlp) with Ge Gao and Anton Abilov

FundamentalsP

_images/module.png

AutodiffP

_images/backprop4.png

TensorsP

_images/stride4.png

EfficiencyP

_images/threadid@3x.png

NetworksP

_images/orig.png _images/mnist2.png _images/mnist5.png _images/
mnist4.png

SlidesP

Slides are available here.

Contents

  * Setup
  * ML Primer
  * Fundamentals
  * Autodiff
  * Tensors
  * Efficiency
  * Networks

_images/match.png

Indices and tablesP

  * Index

  * Module Index

  * Search Page

 

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Setup

(c) Copyright 2021, Sasha Rush.

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