This is somehow related with our team's work(https://fate.suse.com/323437), We want to write a set of restful API for any components in HA product stack use to control the cluster.

Ai and Machine learning play important role in our life, I'd like to learn it. research to see is there any way of using DEEP learning on open source stuff.

Intel Chen Yu developed a user land utility: Introduce the in-kernel hibernation encryption

Mozilla's DeepSpeech project[1] is using TensorFlow and some paper from Baidu to make an open source speech to text system, based on deep learning (TensorFlow). The current project allow the training for own local datasets, but also there is a pre-trained model that can be used during the development. The goal of the project is:

There are customer use cases where sharing information via internet or uploading data somewhere is not acceptable for security reasons: this avoid the usage of some tool like the most famous Google Analytics, and prevent developers from understanding how the web application is used by the customers. I don't want to reinvent the wheel and re-implement a copy of Google Analytics, but getting inspired from it, the goal is to reuse information that we already have to extrapolate an analysis of the WebApp customer usage. I started this project with the aim of learning a programming language where I am not so comfortable yet (python). The purpose of this Hack Week project is to bring this basic tool at a minimal stable and usable state with the purpose of analyze the usage of a WebApp in scenarios where the WebApp is used in an internal network only (offline, disconnected from the internet).

Hacking on the advanced open source computer algebra system -- FriCAS, which has a long history that dates back to 1965. It has not aged very well: 108 C files has been edited only 66 times in the past decade, and full of compiler warnings;

See Jonathan Dieter's chunk project. https://github.com/zchunk/zchunk