tinyfecVPN - A VPN Designed for Lossy Links, with Build-in Forward Error Correction(FEC) Support

•    C++

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A Lightweight VPN with Build-in Forward Error Correction Support(or A Network Improving Tool which works at VPN mode). Improves your Network Quality on a High-latency Lossy Link. tinyfecVPN uses Forward Error Correction(Reed-Solomon code) to reduce packet loss rate, at the cost of additional bandwidth usage.

vpn fec reed-solomon proxy

Schifra Reed Solomon Error Correcting Code Library

•    

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Schifra is a very robust, highly optimized and extremely configurable Reed-Solomon error correcting code library for both software and IP core based applications with implementations in C++ and VHDL.

addition algorithm bch block-code boost

kcptun - A Secure Tunnel Based On KCP with N:M Multiplexing

•    Go

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kcptun maintains a single website — github.com/xtaci/kcptun. Any websites other than github.com/xtaci/kcptun are not endorsed by xtaci. kcptun won't publish anything on any social media.Download precompiled Releases.

kcp fec udp reed-solomon

UDPspeeder - A Tunnel which Improves your Network Quality on a High-latency Lossy Link by using Forward Error Correction,for All Traffics(TCP/UDP/ICMP)

•    C++

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A Tunnel which Improves your Network Quality on a High-latency Lossy Link by using Forward Error Correction. Assume your local network to your server is lossy. Just establish a VPN connection to your server with UDPspeeder + any UDP-based VPN, access your server via this VPN connection, then your connection quality will be significantly improved. With well-tuned parameters , you can easily reduce IP or UDP/ICMP packet-loss-rate to less than 0.01% . Besides reducing packet-loss-rate, UDPspeeder can also significantly improve your TCP latency and TCP single-thread download speed.

udp-tunnel udp tunnel proxy reed-solomon fec

reedsolomon - Reed-Solomon Erasure Coding in Go

•    Go

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Reed-Solomon Erasure Coding in Go, with speeds exceeding 1GB/s/cpu core implemented in pure Go. This is a Go port of the JavaReedSolomon library released by Backblaze, with some additional optimizations.

Sia - Your decentralized private cloud

•    Go

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Sia is a new decentralized cloud storage platform that radically alters the landscape of cloud storage. By leveraging smart contracts, client-side encryption, and sophisticated redundancy (via Reed-Solomon codes), Sia allows users to safely store their data with hosts that they do not know or trust. The result is a cloud storage marketplace where hosts compete to offer the best service at the lowest price. And since there is no barrier to entry for hosts, anyone with spare storage capacity can join the network and start making money.

cloud blockchain decentralized distributed sia storage cloud-storage

rscode

•    C

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A Reed-Solomon error-correcting encoder/decoder library written in C. Useful for forward error correction applications such as streaming audio, magnetic and optical storage, or just plain amusement value. GPL license with commercial license available.

BaconQrCode - QR Code Generator for PHP

•    PHP

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BaconQrCode is a port of QR code portion of the ZXing library. It currently only features the encoder part, but could later receive the decoder part as well. As the Reed Solomon codec implementation of the ZXing library performs quite slow in PHP, it was exchanged with the implementation by Phil Karn.

Play-with-Algorithm-Interview - Codes of my MOOC Course <Play with Algorithm Interviews>

•    C++

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Codes of my MOOC Course <Play with Algorithm Interviews>. Updated contents and practices are also included. 我在慕课网上的课程《玩儿转算法面试》示例代码。课程的更多更新内容及辅助练习也将逐步添加进这个代码仓。

algorithms interview-questions interview-practice leetcode imooc mooc

SPP_net - SPP_net : Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

•    Matlab

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This is a re-implementation of the object detection algorithm described in the ECCV 2014 paper "Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition". This re-implementation should reproduce the object detection results reported in the paper up to some statistical variance. The models used in the paper are trained/fine-tuned using cuda-convnet, while the model attached with this code is trained/fine-tuned using Caffe, for the ease of code release. The implementation of image classification training/testing has not been included, but the network configuration files can be found directly in this code.

Technical_Book_DL - This note presents in a technical though hopefully pedagogical way the three most common forms of neural network architectures: Feedforward, Convolutional and Recurrent

•    TeX

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This note presents in a technical though hopefully pedagogical way the three most common forms of neural network architectures: Feedforward, Convolutional and Recurrent. For each network, their fundamental building blocks are detailed. The forward pass and the update rules for the backpropagation algorithm are then derived in full.

PythonRobotics - Python sample codes for robotics algorithms.

•    Python

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Python codes for robotics algorithm. This is a Python code collection of robotics algorithms, especially for autonomous navigation.

robotics algorithm path-planning control animation localization slam cvxpy ekf autonomous-vehicles autonomous-driving mapping autonomous-navigation

captcha-break - captcha break based on opencv2, tesseract-ocr and some machine learning algorithm.

•    C++

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captcha break based on opencv2, tesseract-ocr and some machine learning algorithm. The simplest captcha breaking.

machine-learning captcha opencv convolutional-neural-networks captcha-break

DeepLearningFlappyBird - Flappy Bird hack using Deep Reinforcement Learning (Deep Q-learning).

•    Python

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This project follows the description of the Deep Q Learning algorithm described in Playing Atari with Deep Reinforcement Learning [2] and shows that this learning algorithm can be further generalized to the notorious Flappy Bird. It is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards.

deep-learning deep-reinforcement-learning game

3D-convolutional-speaker-recognition - :speaker: Deep Learning & 3D Convolutional Neural Networks for Speaker Verification

•    Python

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This repository contains the code release for our paper titled as "Text-Independent Speaker Verification Using 3D Convolutional Neural Networks". The link to the paper is provided as well. The code has been developed using TensorFlow. The input pipeline must be prepared by the users. This code is aimed to provide the implementation for Speaker Verification (SR) by using 3D convolutional neural networks following the SR protocol.

convolutional-neural-networks deep-learning speaker-recognition 3d

Tensorflow-101 - TensorFlow Tutorials

•    Jupyter

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Most of the codes are simple refactorings of Aymeric Damien's Tutorial or Nathan Lintz's Tutorial. There could be missing credits. Please let me know.

tensorflow-tutorials convolutional-neural-networks recurrent-neural-networks

lopq - Training of Locally Optimized Product Quantization (LOPQ) models for approximate nearest neighbor search of high dimensional data in Python and Spark

•    Python

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This is Python training and testing code for Locally Optimized Product Quantization (LOPQ) models, as well as Spark scripts to scale training to hundreds of millions of vectors. The resulting model can be used in Python with code provided here or deployed via a Protobuf format to, e.g., search backends for high performance approximate nearest neighbor search.Locally Optimized Product Quantization (LOPQ) [1] is a hierarchical quantization algorithm that produces codes of configurable length for data points. These codes are efficient representations of the original vector and can be used in a variety of ways depending on the application, including as hashes that preserve locality, as a compressed vector from which an approximate vector in the data space can be reconstructed, and as a representation from which to compute an approximation of the Euclidean distance between points.

nearest-neighbor-search product-quantization lopq clustering spark

Machine-Learning-with-Python - Python codes for common Machine Learning Algorithms

•    Jupyter

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Python codes for common Machine Learning Algorithms

linear-regression polynomial-regression logistic-regression decision-trees random-forest svm svr knn-classification naive-bayes-classifier kmeans-clustering hierarchical-clustering pca lda xgboost-algorithm

Vc - SIMD Vector Classes for C++

•    C++

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Recent generations of CPUs, and GPUs in particular, require data-parallel codes for full efficiency. Data parallelism requires that the same sequence of operations is applied to different input data. CPUs and GPUs can thus reduce the necessary hardware for instruction decoding and scheduling in favor of more arithmetic and logic units, which execute the same instructions synchronously. On CPU architectures this is implemented via SIMD registers and instructions. A single SIMD register can store N values and a single SIMD instruction can execute N operations on those values. On GPU architectures N threads run in perfect sync, fed by a single instruction decoder/scheduler. Each thread has local memory and a given index to calculate the offsets in memory for loads and stores. Current C++ compilers can do automatic transformation of scalar codes to SIMD instructions (auto-vectorization). However, the compiler must reconstruct an intrinsic property of the algorithm that was lost when the developer wrote a purely scalar implementation in C++. Consequently, C++ compilers cannot vectorize any given code to its most efficient data-parallel variant. Especially larger data-parallel loops, spanning over multiple functions or even translation units, will often not be transformed into efficient SIMD code.

vectorization parallel simd-vector simd-instructions simd avx c-plus-plus avx512 sse neon cpp portable cpp11 cpp14 cpp17 avx2 simd-programming data-parallel parallel-computing

Play-with-Data-Structures - Codes of my MOOC Course <Play Data Structures in Java>

•    Java

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Codes of my MOOC Course <Play Data Structures in Java>. Updated contents and practices are also included. 我在慕课网上的课程《Java语言玩转数据结构》示例代码。课程的更多更新内容及辅助练习也将逐步添加进这个代码仓。

algorithm data-structures imooc mooc