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advanced-java icon advanced-java

😮 Core Interview Questions & Answers For Experienced Java(Backend) Developers | 互联网 Java 工程师进阶知识完全扫盲：涵盖高并发、分布式、高可用、微服务、海量数据处理等领域知识

cat icon cat

CAT 作为服务端项目基础组件，提供了 Java, C/C++, Node.js, Python, Go 等多语言客户端，已经在美团点评的基础架构中间件框架（MVC框架，RPC框架，数据库框架，缓存框架等，消息队列，配置系统等）深度集成，为美团点评各业务线提供系统丰富的性能指标、健康状况、实时告警等。

cs-notes icon cs-notes

:books: 技术面试必备基础知识、Leetcode、Java、C++、Python、后端面试、计算机操作系统、计算机网络、系统设计

deeplearning-500-questions icon deeplearning-500-questions

深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，近30万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06

girls-in-ai icon girls-in-ai

免费学代码系列：小白python入门、数据分析data analyst、机器学习machine learning、深度学习deep learning、kaggle实战

java2019 icon java2019

Java知识点总结，面试必备，java基础、java集合、JVM、Java并发、数据结构与算法、LeetCode、剑指offer、计算机网络、设计模式、MySQL、Redis

jeecg-boot icon jeecg-boot

「企业级低代码平台」前后端分离架构SpringBoot 2.x，SpringCloud，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! 引领新的开发模式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，既能快速提高效率，帮助公司节省成本，同时又不失灵活性。

kinecttouch icon kinecttouch

Turns any surface into a giant touchpad using kinect

machine-learning icon machine-learning

:zap:机器学习实战（Python3）：kNN、决策树、贝叶斯、逻辑回归、SVM、线性回归、树回归

pdf icon pdf

编程电子书，电子书，编程书籍，包括C，C#，Docker，Elasticsearch，Git，Hadoop，HeadFirst，Java，Javascript，jvm，Kafka，Linux，Maven，MongoDB，MyBatis，MySQL，Netty，Nginx，Python，RabbitMQ，Redis，Scala，Solr，Spark，Spring，SpringBoot，SpringCloud，TCPIP，Tomcat，Zookeeper，人工智能，大数据类，并发编程，数据库类，数据挖掘，新面试题，架构设计，算法系列，计算机类，设计模式，软件测试，重构优化，等更多分类

pumpkin-book icon pumpkin-book

《机器学习》（西瓜书）公式推导解析，在线阅读地址：https://datawhalechina.github.io/pumpkin-book

seckill icon seckill

Java秒杀系统 高性能高并发

spring-boot-api-project-seed icon spring-boot-api-project-seed

:seedling::rocket:一个基于Spring Boot & MyBatis的种子项目，用于快速构建中小型API、RESTful API项目~

waking-up icon waking-up

计算机基础面试问题全面总结，包含详细的follow-up question以及答案；模拟面试，知识点复习，快速备战面试...

xlearn icon xlearn

High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

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