Sorry about that. Here is the right link: https://github.com/denizyuret/AutoGrad.jl
On Fri, Aug 26, 2016 at 11:38 AM Henri Girard <henri.gir...@gmail.com> wrote: > Your link autograd.jl seems dead ? > > > Le vendredi 26 août 2016 08:51:30 UTC+2, Deniz Yuret a écrit : >> >> Announcing AutoGrad.jl <https://github.com/denizyuret/AutoGrad.jl>: an >> automatic differentiation package for Julia. It is a Julia port of the >> popular Python autograd <https://github.com/HIPS/autograd> package. It >> can differentiate regular Julia code that includes loops, conditionals, >> helper functions, closures etc. by keeping track of the primitive >> operations and using this execution trace to compute gradients. It uses >> reverse mode differentiation (a.k.a. backpropagation) so it can efficiently >> handle functions with array inputs and scalar outputs. It can compute >> gradients of gradients to handle higher order derivatives. >> >> Large parts of the code are directly ported from the Python autograd >> <https://github.com/HIPS/autograd> package. I'd like to thank autograd >> author Dougal Maclaurin for his support. See (Baydin et al. 2015) >> <https://arxiv.org/abs/1502.05767> for a general review of automatic >> differentiation, autograd tutorial >> <https://github.com/HIPS/autograd/blob/master/docs/tutorial.md> for some >> Python examples, and Dougal's PhD thesis for design principles. JuliaDiff >> <http://www.juliadiff.org/> has alternative differentiation tools for >> Julia. >> >> best, >> deniz >> >>
