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Source-to-Source Debuggable Derivatives in Pure Python

9 mentions on Hacker News by 9 people: 6 stories, 3 comments · first 2017-11-06 · latest 2021-12-25

Mentions per year 9 mentions · busiest year 2018 (4)
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  1. …more optimized kernels (and kernels that act on GPUs). The AD that I was referencing was tangent ( https://github.com/google/tangent ). It was an interesting project but it's hard to see who the audience is. Generating Python source code…
    comment by ChrisRackauckas · 2021-12-25 · in Trade-Offs in Automatic Differentiation: TensorFlow, PyTorch, Jax, and Julia
  2. …[1] - https://www.youtube.com/watch?v=z8GyNneq5D4 [2] - https://stackoverflow.com/a/45134000 [3] - https://github.com/google/tangent EDIT: 1. Added reference. 2. Formatting
    comment by scarecrow112 · 2019-11-04 · in Deep Learning in the Real World: Dealing with Non-Differentiable Loss Functions
  3. ▲ 1
    Tangent: Automatic Differentiation with Python
    story by dweekly · 2018-03-20 · 0 comments
  4. ▲ 2
    Tangent: open-source Python library for automatic differentiation
    story by rusht · 2018-03-20 · 0 comments
  5. ▲ 1
    story by cs702 · 2018-02-19 · 0 comments
  6. I believe https://github.com/google/tangent counts, though I'm not 100% sure.
    comment by joshuamorton · 2018-01-06 · in “Deep Learning has outlived its usefulness as a buzz-phrase”
  7. ▲ 1
    Source-To-Source Automatic Differentiation in Python
    story by alexbw · 2017-11-18 · 0 comments
  8. ▲ 1
    Show HN: Source-To-Source Debuggable Derivatives in Pure Python
    story by rhakmi · 2017-11-07 · 0 comments
  9. ▲ 3
    Google Research - Tangent: Source-To-Source Debuggable Derivatives
    story by activatedgeek · 2017-11-06 · 0 comments