What is tinyFecVPN?

Overview of lightweight high-performance VPN with build-in forward error correction support
30 October 2017   2910

What is tinyFecVPN?

TinyFecVPN Improves your Network Quality on a High-Latency Lossy Link by using Forward Error Correction.

tinyFecVPN architecture
tinyFecVPN architecture 

It uses same lib as UDPspeeder, supports all FEC features of UDPspeeder. TinyFecVPN works at VPN mode,while UDPspeeder works at UDP tunnel mode.

It is supported by Linux host (including desktop Linux, OpenWRT router, or Raspberry PI). For Windows and MacOS You can run TinyFecVPN inside 7.5mb virtual machine image.

TinyFecVPN uses FEC(Forward Error Correction) to reduce packet loss rate, at the cost of addtional bandwidth. The algorithm for FEC is called Reed-Solomon.

TinyFecVPN
TinyFecVPN

If you are interested, visit GitHub for more information.

TensorFlow 2.0 to be Released

New major release of the machine learning platform brought a lot of updates and changes, some stuff even got cut
01 October 2019   172

A significant release of the TensorFlow 2.0 machine learning platform is presented, which provides ready-made implementations of various deep machine learning algorithms, a simple programming interface for building models in Python, and a low-level interface for C ++ that allows you to control the construction and execution of computational graphs. The system code is written in C ++ and Python and is distributed under the Apache license.

The platform was originally developed by the Google Brain team and is used in Google services for speech recognition, facial recognition in photographs, determining the similarity of images, filtering spam in Gmail, selecting news in Google News and organizing the translation taking into account the meaning. Distributed machine learning systems can be created on standard equipment, thanks to the built-in support in TensorFlow for spreading computing to multiple CPUs or GPUs.

TensorFlow provides a library of off-the-shelf numerical computation algorithms implemented through data flow graphs. The nodes in such graphs implement mathematical operations or entry / exit points, while the edges of the graph represent multidimensional data arrays (tensors) that flow between the nodes. The nodes can be assigned to computing devices and run asynchronously, simultaneously processing all the suitable tensors at the same time, which allows you to organize the simultaneous operation of nodes in the neural network by analogy with the simultaneous activation of neurons in the brain.

Get more info about the update at official website.