dev-python/autograd (science)

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Package Information

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.
Homepage:
https://github.com/HIPS/autograd

Versions

Version EAPI Keywords Slot
9999 8 ~amd64 0
1.8.0 8 ~amd64 0

Metadata

Description

Maintainers

Upstream

Raw Metadata XML
<pkgmetadata>
	<maintainer type="project">
		<email>sci@gentoo.org</email>
		<name>Gentoo Science Project</name>
	</maintainer>
	<maintainer type="person">
		<email>apn-pucky@gentoo.org</email>
		<name>Alexander Puck Neuwirth</name>
	</maintainer>
	<longdescription lang="en">
	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. 
	</longdescription>
	<upstream>
		<remote-id type="pypi">autograd</remote-id>
		<remote-id type="github">HIPS/autograd</remote-id>
	</upstream>
</pkgmetadata>

Lint Warnings

Manifest

Type File Size Versions
DIST autograd-1.8.0.gh.tar.gz 2559484 bytes 1.8.0
Unmatched Entries
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