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activiti icon activiti

Activiti is a light-weight workflow and Business Process Management (BPM) Platform targeted at business people, developers and system admins. Its core is a super-fast and rock-solid BPMN 2 process engine for Java. It's open-source and distributed under the Apache license. Activiti runs in any Java application, on a server, on a cluster or in the cloud. It integrates perfectly with Spring, it is extremely lightweight and based on simple concepts.

aizooservice icon aizooservice

AIZooService,封装了基本的chatbot模型,以及socket链接,线程池化,以应对高并发场景

awesome-java-books icon awesome-java-books

Java开发者技术书籍大全 - Java入门书籍,Java基础及进阶书籍,框架与中间件,架构设计,设计模式,数学与算法,JVM周边语言,项目管理&领导力&流程,职业素养与个人成长,格局与视野,面试参考书等。

blade icon blade

:rocket: Lightning fast and elegant mvc framework for Java8

curator icon curator

ZooKeeper client wrapper and rich ZooKeeper framework

deepqa icon deepqa

基于深度学习的知识问答系统(KBQA based Deep Learning)

discourse icon discourse

A platform for community discussion. Free, open, simple.

drqa icon drqa

Facebook的QA的论文实现源码:Reading Wikipedia to Answer Open-Domain Questions

drqa-pytorch-nanj icon drqa-pytorch-nanj

国内南大同学的对DrQA论文的实现 A pytorch implementation of Reading Wikipedia to Answer Open-Domain Questions.

elasticsearch-analysis-jieba icon elasticsearch-analysis-jieba

The plugin includes the `jieba` analyzer, `jieba` tokenizer, and `jieba` token filter, and have two mode you can choose. one is `index` which means it will be used when you want to index a document. another is `search` mode which used when you want to search something.

elasticsearch-hadoop icon elasticsearch-hadoop

:elephant: Elasticsearch real-time search and analytics natively integrated with Hadoop

examples icon examples

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

fastjson icon fastjson

:bullettrain_side: A fast JSON parser/generator for Java

fescar icon fescar

:fire: Fescar is an easy-to-use, high-performance, java based, open source distributed transaction solution.

flink-recommandsystem-demo icon flink-recommandsystem-demo

:helicopter::rocket:基于Flink实现的商品实时推荐系统。flink统计商品热度,放入redis缓存,分析日志信息,将画像标签和实时记录放入Hbase。在用户发起推荐请求后,根据用户画像重排序热度榜,并结合协同过滤和标签两个推荐模块为新生成的榜单的每一个产品添加关联产品,最后返回新的用户列表。

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