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Efficiently computes derivatives of numpy code 2.47 MB built 2026-10-29 13:19:08 GMT

Description

Autograd can automatically differentiate native Python and Numpy code. It can handle a large subset of Python's features, including loops, ifs, recursion and closures, and it can even take derivatives of derivatives of derivatives. It supports reverse-mode differentiation (a.k.a. backpropagation), which means it can efficiently take gradients of scalar-valued functions with respect to array-valued arguments, as well as forward-mode differentiation, and the two can be composed arbitrarily. The main intended application of Autograd is gradient-based optimization.

Dependencies

20 total
Requires17
  • (python3dist(tomli) if python3-devel < 3.11)
  • pyproject-rpm-macros
  • python3-devel
  • python3-devel
  • python3dist(hatchling)
  • python3dist(numpy) < 3~~
  • python3dist(packaging)
  • python3dist(pip) >= 19
  • python3dist(pytest)
  • python3dist(pytest-xdist)
  • python3dist(scipy)
  • python3dist(tomcli)
  • rpmlib(CompressedFileNames) <= 3.0.4-1
  • rpmlib(DynamicBuildRequires) <= 4.15.0-1
  • rpmlib(FileDigests) <= 4.6.0-1
  • rpmlib(RichDependencies) <= 4.12.0-1
  • rpmlib(TildeInVersions) <= 4.10.0-1
Provides3
  • python-autograd-doc = 1.8.0-6.fc45
  • python3-autograd = 1.8.0-6.fc45
  • python3-autograd+scipy = 1.8.0-6.fc45
ObsoletesNo Obsoletes
ConflictsNo Conflicts
RecommendsNo Recommends
SuggestsNo Suggests
SupplementsNo Supplements
EnhancesNo Enhances

Files

2
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