larryliu2018

2048

alasql

AlaSQL.js - JavaScript SQL database for browser and Node.js. Handles both traditional relational tables and nested JSON data (NoSQL). Export, store, and import data from localStorage, IndexedDB, or Excel.

base-drafts

Internet-Drafts that make up the base QUIC specification

bootstrap

The most popular HTML, CSS, and JavaScript framework for developing responsive, mobile first projects on the web.

brinkhoff

Brinkhoff Moving Objects Generator

Capsule_Net

Geoffrey Hinton，深度学习的开创者之一，反向传播等神经网络经典算法的发明人，2017年10月发表了论文，介绍了全新的胶囊网络模型，以及相应的囊间动态路由算法。本人用Paddle框架实现了它

deepframeworks

Evaluation of Deep Learning Frameworks

learngit

My_C_program

report some little program using C/C++

sdn-handbook

SDN手册

larryliu2018/Capsule_Net

Geoffrey Hinton，深度学习的开创者之一，反向传播等神经网络经典算法的发明人，2017年10月发表了论文，介绍了全新的胶囊网络模型，以及相应的囊间动态路由算法。本人用Paddle框架实现了它

larryliu2018/sdn-handbook

SDN手册

larryliu2018/base-drafts

Internet-Drafts that make up the base QUIC specification

larryliu2018/dash.js

A reference client implementation for the playback of MPEG DASH via Javascript and compliant browsers.

larryliu2018/Ebook

📚 各类图书

larryliu2018/flash-linux0.11-talk

你管这破玩意叫操作系统源码 — 像小说一样品读 Linux 0.11 核心代码

larryliu2018/floodlight

Floodlight SDN OpenFlow Controller

larryliu2018/gns3-gui

GNS3 Graphical Network Simulator

larryliu2018/Learning-Python-Networking-Second-Edition

Learning Python Networking - Second Edition, published by Packt

larryliu2018/linux_kernel_wiki

linux内核学习资料：200+经典内核文章，100+内核论文，50+内核项目，500+内核面试题，80+内核视频

larryliu2018/loxigen

OpenFlow protocol bindings for multiple languages

larryliu2018/Machine-Learning-Algorithms

Types of Machine Learning Algorithms There are 3 types of machine learning (ML) algorithms: Supervised Learning Algorithms: Supervised learning uses labeled training data to learn the mapping function that turns input variables (X) into the output variable (Y). In other words, it solves for f in the following equation: Y = f (X) This allows us to accurately generate outputs when given new inputs. We’ll talk about two types of supervised learning: classification and regression. Classification is used to predict the outcome of a given sample when the output variable is in the form of categories. A classification model might look at the input data and try to predict labels like “sick” or “healthy.” Regression is used to predict the outcome of a given sample when the output variable is in the form of real values. For example, a regression model might process input data to predict the amount of rainfall, the height of a person, etc. The first 5 algorithms that we cover in this blog – Linear Regression, Logistic Regression, CART, Naïve-Bayes, and K-Nearest Neighbors (KNN) — are examples of supervised learning. Ensembling is another type of supervised learning. It means combining the predictions of multiple machine learning models that are individually weak to produce a more accurate prediction on a new sample. Algorithms 9 and 10 of this article — Bagging with Random Forests, Boosting with XGBoost — are examples of ensemble techniques. Unsupervised Learning Algorithms: Unsupervised learning models are used when we only have the input variables (X) and no corresponding output variables. They use unlabeled training data to model the underlying structure of the data. We’ll talk about three types of unsupervised learning: Association is used to discover the probability of the co-occurrence of items in a collection. It is extensively used in market-basket analysis. For example, an association model might be used to discover that if a customer purchases bread, s/he is 80% likely to also purchase eggs. Clustering is used to group samples such that objects within the same cluster are more similar to each other than to the objects from another cluster. Dimensionality Reduction is used to reduce the number of variables of a data set while ensuring that important information is still conveyed. Dimensionality Reduction can be done using Feature Extraction methods and Feature Selection methods. Feature Selection selects a subset of the original variables. Feature Extraction performs data transformation from a high-dimensional space to a low-dimensional space. Example: PCA algorithm is a Feature Extraction approach. Algorithms 6-8 that we cover here — Apriori, K-means, PCA — are examples of unsupervised learning. Reinforcement learning: Reinforcement learning is a type of machine learning algorithm that allows an agent to decide the best next action based on its current state by learning behaviors that will maximize a reward. Reinforcement algorithms usually learn optimal actions through trial and error. Imagine, for example, a video game in which the player needs to move to certain places at certain times to earn points. A reinforcement algorithm playing that game would start by moving randomly but, over time through trial and error, it would learn where and when it needed to move the in-game character to maximize its point total.

larryliu2018/MiniDNN

A header-only C++ library for deep neural networks

larryliu2018/net

[mirror] Go supplementary network libraries

larryliu2018/paho.mqtt.golang

larryliu2018/pox

The POX network software platform

larryliu2018/quic-go

A QUIC implementation in pure go

larryliu2018/quic-interop-runner

QUIC interop runner

larryliu2018/quiche

larryliu2018/Reinforcement-Learning-Approach-to-Packet-Routing-on-a-Dynamic-Network

Packet routing simulation on a dynamic network using Shortest Path Routing, Q-learning, and Deep Q-learning

larryliu2018/rl-fec

Learning Based FEC for Non-Terrestrial Networks with Delayed Feedback

larryliu2018/shadowsocks-windows

A C# port of shadowsocks

larryliu2018/tensorboard

TensorFlow's Visualization Toolkit

larryliu2018/TopPaper

Classic papers for beginners, and impact scope for authors.

larryliu2018/trajectory-networks

A set of tools to generate synthetic trajectory data by simulating moving objects and analyse the results using trajectory networks.

larryliu2018/unpv13e

Unix Network Programming, Volume 1: The Sockets Networking API (3rd Edition)

larryliu2018/volantmq

High-Performance MQTT Server

larryliu2018/wait-for-it

Pure bash script to test and wait on the availability of a TCP host and port

larryliu2018/xquic

larryliu2018/yomo

🦖 Serverless Streaming Framework for Low-latency Edge Computing applications, running atop QUIC protocol, as Metaverse infrastructure, engaging 5G technology.