./wip/py-autograd, Efficiently computes derivatives of numpy code

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Branch: CURRENT, Version: 1.3, Package name: py37-autograd-1.3, Maintainer: jihbed.research

Autograd can automatically differentiate native Python
and Numpy code. It can handle a large subset of Python
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

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SHA1: 9dc88df4078c111f45731e0934cf8c0fd9a87723
RMD160: 4566e05e7ca36c37b3b804d60b7610cc8b3c04ef
Filesize: 37.36 KB

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