Search Results for "main"

...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.

This repository is for the GitHub Action to run a Super-Linter. It is a simple combination of various linters, written in bash, to help validate your source code. The super-linter finds issues and reports them to the console output. Fixes are suggested in the console output but not automatically fixed, and a status check will show up as failed on the pull request. The design of the Super-Linter is currently to allow linting to occur in GitHub Actions as a part of continuous integration...

Gixy is a tool to analyze Nginx configuration. The main goal of Gixy is to prevent security misconfiguration and automate flaw detection. Currently supported Python versions are 2.7, 3.5, 3.6 and 3.7. Gixy is well tested only on GNU/Linux, other OSs may have some issues. You can find things that Gixy is learning to detect at Issues labeled with "new plugin". By default Gixy will try to analyze Nginx configuration placed in /etc/nginx/nginx.conf.