https://github.com/hzdr/autodiff101

An introduction to Automatic Differentiation with theory and code examples.
https://github.com/hzdr/autodiff101

autodiff autograd automatic-differentiation jax pytorch

Last synced: about 1 month ago
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An introduction to Automatic Differentiation with theory and code examples.

• Host: GitHub
• URL: https://github.com/hzdr/autodiff101
• Owner: hzdr
• License: bsd-3-clause
• Created: 2021-06-07T13:53:25.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2024-07-12T20:32:27.000Z (about 1 year ago)
• Last Synced: 2025-07-26T16:21:40.366Z (2 months ago)
• Topics: autodiff, autograd, automatic-differentiation, jax, pytorch
• Language: TeX
• Homepage:
• Size: 115 KB
• Stars: 5
• Watchers: 2
• Forks: 1
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# An introduction to Automatic Differentiation

`theory/`: AD background theory, introducing the concept of forward and reverse

mode plus Jacobian-vector / vector-Jacobian products. To go deeper, make sure

to check out the excellent [JAX autodiff cookbook][jax_autodiff_cookbook] as

well as @mattjj's [talk on autograd][mattjj_talk].

`talk/`: Talk version of those notes. The talk was given at the @hzdr

 local unit's Machine Learning journal club and at a @hzdr

and @casus workshop on physics-informed neural networks, both organized by Nico

Hoffmann (@nih23).

`examples/`: AD examples using [autograd], [jax] and [pytorch]. The examples

focus mostly on how to define custom derivatives in jax (and autograd). This

has helped to understand how Jacobian-vector products actually work. More

examples to come!

Download `talk` and `theory` PDF files from the [Releases page][releases] or

the latest [CI run][actions]. You can also click the badges above. The talk is

also [available via figshare][talk_figshare].

[autograd]: https://github.com/HIPS/autograd

[jax]: https://github.com/google/jax

[pytorch]: https://github.com/pytorch/pytorch

[jax_autodiff_cookbook]: https://jax.readthedocs.io/en/latest/notebooks/autodiff_cookbook.html

[mattjj_talk]: http://videolectures.net/deeplearning2017_johnson_automatic_differentiation

[releases]: https://github.com/hzdr/autodiff101/releases/latest

[actions]: https://github.com/hzdr/autodiff101/actions

[talk_figshare]: https://figshare.com/articles/presentation/An_introduction_to_Automatic_Differentiation/14802948