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With its updated version of Autograd, JAX can automatically differentiate native Python and NumPy functions. It can differentiate through loops, branches, recursion, and closures, and it can take derivatives of derivatives of derivatives. It supports reverse-mode differentiation (a.k.a. backpropagation) via grad as well as forward-mode differentiation, and the two can be composed arbitrarily to any order. What’s new is that JAX uses XLA to compile and run your NumPy programs on GPUs and...

...Some attempts to rewrite it in other languages were done, but currently (since January 2012) there is an attempt or rewrite using those technologies: _ C++11 _ wxWidgets 2.9 (will be 3.0 when it will be released) _ openGL _ pluma framework (a fixed version located on bitbucket) The design of the actual C++ code-base primarily targets clean source code and high modularity of source code, to allow easy reuse of the code and fast learning for people who might join the effort. Current development is located on https://bitbucket.org/bmorel/autorealm . Howerver, older repositories are still here in case someone would like to consult them, and to allow easy access to algorithms they might contain.