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>`C\?\d  O $4 < D  T ' (*,,/T/|112 2(28293(:6F:[G:H<I>X?0Y?D\?t]A^LYbORdQAeQFfQKlQNtQhuSxvUwUxWyYxz[[[\\\\\"\d\hCpython3-autograd1.7.01.fc41Efficiently 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.gHbuildvm-x86-13.iad2.fedoraproject.org nFedora ProjectFedora ProjectMITFedora ProjectUnspecifiedhttps://github.com/HIPS/autogradlinuxnoarchAWH,H,[\` --` & & JJ##]](*#((%%,X*Z?0S %&>(977 3ii l lpp 224)  n 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