zfu1316 Goto Github PK

100-days-of-code-frontend

Curriculum for learning front-end development during #100DaysOfCode.

18.06-linalg-notes

MIT-18.06-线性代数-完整笔记

airbnb

algo-quicksheet

Quicksheet for Algorithms

algorithm

algorithm-1

algorithm-ood-java

algorithm-stone

ACM/LeetCode算法竞赛路线图，最全的算法学习地图！

algorithm_interview_notes-chinese

2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记

architecture.of.internet-product

互联网公司技术架构，微信/淘宝/微博/腾讯/阿里/美团点评/百度/Google/Facebook/Amazon/eBay的架构，欢迎PR补充

articles

distributed system;big data;machine learning

awesome-design-patterns

A curated list of software and architecture related design patterns.

awesome-distributed-systems

A curated list of awesome distributed systems books, papers, resources and shiny things.

awesome-leading-and-managing

Awesome List of resources on leading people and being a manager. Geared toward tech, but potentially useful to anyone.

awesome-microservices

A curated list of Microservice Architecture related principles and technologies.

baobao

bookstore-nest

chelizi

code_collection

code misc/projects/demos

crash-course-computer-science-chinese

:computer: 计算机速成课 | Crash Course 字幕组 (全40集 2018-5-1 精校完成)

design-patterns-for-humans-cn

🎉 Design patterns for humans 机翻中文版

designpattern

Java 设计模式归纳 (观察者、工厂、单例、策略、适配器、命令、装饰者、外观、模板方法、状态).

developer-roadmap

Roadmap to becoming a web developer in 2019

distributed-systems

MIT课程《Distributed Systems 》学习和翻译

docker-compose-files

Some typical docker compose templates.

easyscheduler

Easy Scheduler是一个分布式工作流任务调度系统，主要解决"错综复杂的任务依赖关系，而不能直观监控任务健康状态等问题"。Easy Scheduler以DAG流式的方式将Task组装起来，并可实时监控任务的运行状态，同时支持重试、从指定节点恢复失败、暂停及Kill任务等操作。EasyScheduler由在工作流调度方面工作多年的多位小伙伴研发而成，致力于成为大数据平台的中流砥柱，使调度变得更加容易，更可以从其中文名“易调度”看出我们的初衷，如果你对目前市面上的调度不够满意，非常欢迎使用易调度，欢迎大家加入进来，提出需求，也欢迎贡献代码

educative.io_courses

this is downloadings of all educative.io free student subscription courses as pdf from GitHub student pack

flink-commodity-recommendation-system

:whale:基于 Flink 的商品实时推荐系统。当用户产生评分行为时，数据由 kafka 发送到 flink，根据用户历史评分行为进行实时和离线推荐。实时推荐包括：基于行为和实时热门，离线推荐包括：历史热门、历史优质商品和 itemcf 。

from_coder_to_expert

2019年最新总结，从程序员到CTO，从专业走向卓越，分享大牛企业内部pdf与PPT

geektime-spring-family

极客时间视频课程《玩转Spring全家桶》