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陈安云's Projects

antlr4 icon antlr4

ANTLR (ANother Tool for Language Recognition) is a powerful parser generator for reading, processing, executing, or translating structured text or binary files.

cheatsheets-ai icon cheatsheets-ai

Essential Cheat Sheets for deep learning and machine learning researchers

cling icon cling

UPnP/DLNA library for Java and Android

deepfacelab icon deepfacelab

DeepFaceLab is the leading software for creating deepfakes.

grammars-v4 icon grammars-v4

Grammars written for ANTLR v4; expectation that the grammars are free of actions.

java-concurrency-progamming-tutorial icon java-concurrency-progamming-tutorial

!!!Java 高并发多线程编程系列 demo 代码&教程&面试题集锦! !! 包括但不限于线程安全性, atomic包下相关类、CAS原理、Unsafe类、synchronized关键字等的使用及注意事项,volatile关键字的规则和使用,及synchronized关键字的可见性,happens-before原则 安全发布对象的一些核心方法方式,线程安全策略定义不可变对象、线程封闭、同步容器、并发容器等.\,AQS模型设计及相关同步组件的原理和使用,都非常实用,具体包括:CountDownLatch、Semaphore、CyclicBarrier、ReentrantLock与锁、Condition等,FutureTask、Fork/Join框架、BlockingQueue,其中FutureTask讲解时会对比着Callable、Runnable、Future来讲,线程调度-线程池 J.U.C里最后一部分:线程池,ThreadPoolExecutor详细介绍(参数、状态、方法)、线程池类图、Executor框架接口等进行讲解,需要大家能了解线程池的许多细节及配置,并能在实际项目中正确使用 多线程并发拓展讲解 对并发编程做些补充,但都贴近当前的面试,主要讲解死锁产生的条件及预防、多线程并发编程的最佳实践、Spring与线程安全、以及面试都特别喜欢问的HashMap和ConcurrentMap源码细节。当然,面试喜欢问的问题,对实际项目开发也是特别重要的 高并发之扩容 高并发部分:思路,侧重面试,扩容思路,首先介绍垂直扩容和水平扩容的区别,之后介绍数据库读操作扩展和写操作扩展思路。 高并发之缓存 思路,本章讲解高并发中缓存方案。 包含对缓存特征(命中率、最大元素、清空策略)、影响缓存命中率因素、缓存分类和应用场景(本地缓存、分布式缓存)、高并发场景下缓存常见问题(缓存一致性、缓存并发、缓存穿透、雪崩)等的具体介绍。此外,针对大家常用的缓存组件Guava Cache、Memcache、Redis 高并发之消息队列 思路,本章介绍了消息队列的特性(业务无关、FIFO、容灾、性能)、为什么需要消息队列以及消息队列的好处(业务解耦、最终一致性、广播、错峰与流控),最后对当前比较流行的消息队列组件kafka和rabbitmq做了架构分析和特性介绍 高并发之应用拆分 从实际项目拆分步骤讲起,让大家可以实际感受到应用拆分的好处和解决的问题,之后引出对应用拆分原则(业务优先、循序渐进、兼顾技术、可靠测试)和应用拆分时思考的内容(应用之间通信、应用之间数据库设计、避免事务跨应用),并引出对服务化Dubbo和微服务Spring Cloud的框架介绍 高并发之应用限流,自动降级(超时、失败次数、故障、限流)和人工降级(开关)

jeecg-boot icon jeecg-boot

基于代码生成器的快速开发平台,超越传统商业开发平台!前后端分离架构:SpringBoot 2.x,Ant Design&Vue,Mybatis-plus,Shiro,JWT。强大的代码生成器让前后端代码一键生成,无需写任何代码! 引领新开发模式(OnlineCoding-> 代码生成-> 手工MERGE),帮助Java项目解决70%重复工作,让开发更关注业务逻辑,既能快速提高开发效率,帮助公司节省成本,同时又不失灵活性。

models icon models

Models and examples built with TensorFlow

otter icon otter

阿里巴巴分布式数据库同步系统(解决中美异地机房)

randomcnn-voice-transfer icon randomcnn-voice-transfer

Audio style transfer with shallow random parameters CNN. Result: https://soundcloud.com/mazzzystar/sets/speech-conversion-sample

simple-java-maven-app icon simple-java-maven-app

For an introductory tutorial on how to use Jenkins to build a simple Java application with Maven.

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