Search Results for "continue"

...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. For more information, check out the tutorial and the examples directory. We can continue to differentiate as many times as we like, and use numpy's vectorization of scalar-valued functions across many different input values.

...We received a lot of positive feedback for the multi-stage build feature introduced in April and had proposals and user requests for many similar additions. But before that, we needed to make sure that we have capabilities to continue adding such features in the future and a solid foundation to extend on. Quite soon it was clear that we would need to redefine most of the fundamentals about how we even define a build operation and needed a clean break from the current codebase. A proposal was written, and development started in the open under the new repository.

...Infer checks for null pointer dereferences, memory leaks, coding conventions and unavailable API’s. Start with the Getting Started guide and our other docs to download and try Infer yourself. Infer is still evolving, and we want to continue to develop it in the open. We hope it will be useful for other projects, so please try it out or contribute to it, join the community and give us feedback!

News: since 2015 we continue all related developments within Collective Knowledge Framework: http://github.com/ctuning/ck/wiki ICI transforms production compilers into open interactive plugin-based research infrastructure as the first step towards future intelligent self-tuning computing systems. More information: http://cTuning.org/ici