Repository Index

TranslatedJava backend knowledge graph🔥 Helping Java beginners grow

Translated"Source Code for the Simplest Spring Cloud Tutorial Ever"

• spring-cloud
• spring-boot
• hystrix
• finchley

TranslatedAn easy-to-understand, witty and humorous Java learning guide, covering core knowledge points such as Java basics, Java concurrent programming, the Java Virtual Machine, Java enterprise development, and Java interviews. To learn Java, look no further than 二哥's (Erge, "Second Brother") Java advancement path 😄

• java
• jvm
• springboot
• mysql

TranslatedBig Data Beginner's Guide :star:

• hadoop
• hdfs
• yarn
• mapreduce

Modern Java - A Guide to Java 8

• java-8
• tutorial
• lambda-expressions
• java

TranslatedSpring Boot practical learning examples, the best practice for Spring Boot beginners and for consolidating core technologies.

• springboot
• redis
• jpa
• elasticsearch

a collection of simple demos of React.js

Translated"Spring Boot Basic Tutorial", the 2.x version is being published serially and is still ongoing! Click the link below to go straight to the tutorial table of contents!

• spring-boot
• spring-cloud
• spring-data-jpa
• dubbo

Translated"Learn Node.js Together"

• tutorial
• nodejs
• mongodb
• express

Translatedflink learning blog. http://www.54tianzhisheng.cn/ Covers Flink introduction, concepts, principles, hands-on practice, performance tuning, source code analysis, and more. Includes learning examples involving Flink Connector, Metrics, Library, DataStream API, Table API & SQL, and also shares large-scale project cases of Flink in production (PV/UV, log storage, real-time deduplication of 10 billion records, monitoring and alerting). You are welcome to support my column 《大数据实时计算引擎 Flink 实战与性能优化》 ("Big Data Real-Time Computing Engine Flink: Hands-On Practice and Performance Optimization")

• flink
• kafka
• elasticsearch
• spark

Microservice Architecture with Spring Boot, Spring Cloud and Docker

• spring-boot
• spring-cloud
• microservices
• docker

Android Transition animations explanation with examples.

Translatedmall learning tutorial, with a comprehensive analysis of the architecture, business logic, and key technical points. The mall project (60k+ stars) is an e-commerce system built with currently mainstream technologies. It covers technologies such as SpringBoot, MyBatis, Elasticsearch, RabbitMQ, Redis, MongoDB, and MySQL, and uses Docker containerized deployment.

• springboot
• mybatis
• elasticsearch
• rabbitmq

This is about useful JS tips!

• javascript
• javascript-tips
• jstips
• hacks

TranslatedTikTok unlock without removing the SIM card, with the latest support for iPhone & iPad, TikTok & TikTok TestFlight, region switching, video posting, live streaming, likes, comments, private message chat, and more!

• tiktok
• tiktok-unlock
• unlock

Translated:books: This repository is a summary of the technical learning journey of the author, Xiao Fuge (小傅哥), from many years of working in front-line internet Java development. It aims to give everyone a clear and detailed learning tutorial, with a focus on writing about core Java content. If this repository helps you, please show your support (follow, like, share)!

• java
• javafx
• spring
• mybaits

TranslatedThe Modern JavaScript Tutorial, based on the latest ECMAScript specification, explains JavaScript knowledge from the basics to advanced topics through content that is simple but sufficiently detailed.

• javascript
• tutorial
• chinese

mal - Make a Lisp

• mal
• docker
• lisp
• lisp-interpreter

TranslatedA programmer who loves cooking and is determined to use animations to explain algorithms in plain, easy-to-understand terms. My interview website: www.chengxuchu.com

• leetcode
• offer
• algorithms
• base

The repository host gives no description.

:snowflake: a short introduction to node.js

The repository host gives no description.

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

how to build modular applications with browserify