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'p~[ye6 'p~[yC[vJQ`cwha^$HM0c;_3ݙ* תRrqݰ-Fd"6{Pr# H> gt|fkɺ6ƟM0ܭ S$1a}NJ-8b虭bǶ#FNJWf+6~2OjrQN$Ҭeҿ\Y;R06R?5ixt$K >`C`?`d  O $8 B L  ` ' (l*-//12P2p3*(3b83l93:9F>)G>\H@pIBXC YC \CT]Eh^PKbSDdUeUfUlU!tU<uWPvYdwYx[y]yz_____``` `L`PCpython3-autograd1.6.26.fc40Efficiently computes derivatives of numpy codeAutograd can automatically differentiate native Python and Numpy code. It can handle a large subset of Python's features, including loops, ifs, recursion and closures, and it can even take derivatives of derivatives of derivatives. It supports reverse-mode differentiation (a.k.a. backpropagation), which means it can efficiently take gradients of scalar-valued functions with respect to array-valued arguments, as well as forward-mode differentiation, and the two can be composed arbitrarily. The main intended application of Autograd is gradient-based optimization.e ubuildvm-x86-18.iad2.fedoraproject.org ;Fedora ProjectFedora ProjectMITFedora ProjectUnspecifiedhttps://github.com/HIPS/autogradlinuxnoarch\ GG[\s==&&   6 /!C0>..  ` &m&mKOKO ]G]G13hh$w$w,X *6 Sl&f(I:&:& 3 ;$eeVV  _ _ttMM))4)  n 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