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This project is a summary of the author's years of study and work practice.
Most of the code has been actually run to ensure accuracy.
Guys who point star regularly have opportunity to receive gifts by random algorithms. 😁



  -- bigdata               solution for bigdata,contains hive/hadoop/spark/hbase etc... 
  -- code-languages        programming language,contains java/python/scala etc...  
  -- cs-other              some interesting things in the cs field    
  -- deep-learning         derivation of some algorithm principles of deep learning,tensorflow frame etc...    
  -- feature-engineering   feature-engineering is very important for algorithm
  -- math                  mathematical principles,contains matrix analysis, probability and statistics etc...
  -- mathcasebycase        some mathematical knowledge points that are relatively scattered and difficult to classify 
  -- recommend             knowledge about recommendation system  
  -- service-enginnering   service online,essential knowledge for algorithm online  
  -- tools                 various tools commonly used in practice, 
                           including awk, grep, sed data processing three swordsmen, 
                           git, maven and other common tools, intellij,
                           sublime, vim and other IDEs, 
                           linux-shell common scripts
  -- traditional-algorithm traditional machine learning algorithms that are different from deep learning,
                           including clustering algorithms/optimization methods/tree algorithms, etc.,
                           as well as a brief introduction to mllib.

  -- bigdata               大数据处理方案,包括hive/hadoop/spark/hbase等
-- code-languages 编码语言,包括java/python/scala等
-- cs-other cs领域的一些有意思的事情
-- deep-learning 深度学习的一些算法原理推导,tensorflow等框架
-- feature-engineering 特征工程,做过算法的人都知道特征工程重要性
-- math 数学原理,包括矩阵分析,概率统计等算法中常用数学知识
-- mathcasebycase 一些比较分散不好归类的数学知识点
-- recommend 推荐系统一些相关知识,目前作者就从事推荐相关工作
-- service-enginnering 线上服务,算法上线必备知识
-- tools 实际中常用的各种工具,包括awk,grep,sed数据处理三剑客,git,maven等常见工具, intellij,sublime,vim等IDE, linux-shell常见脚本
-- traditional-algorithm 区别于深度学习的传统机器学习算法,包括聚类算法/最优化方法/树类算法等, 还有mllib的简单介绍。

Suitable for

for computer science guys

The author come from a non-CS major,
and did not systematically study data structures, operating systems,
design patterns and other courses during school.
The relevant knowledge is to be studied systematically after work.
So this project is especially suitable for non-CS majors.


for non-computer science guys

For CS majors, some of them have not studied mathematics courses systematically,
such as matrix analysis, probability statistics, optimization, etc.
The major of the author's master's degree is pattern recognition.
And in the future work, he roughly understand which mathematical knowledge
is the key and difficult point in algorithm learning and practice.
Therefore, this project is also suitable for students majoring in CS


for guys who need to put the algorithm online from 0 to 1

The algorithm is not just an offline train model,
it can even be said that the offline train model is only a small part of the work.
On the contrary, the corresponding engineering capabilities,
code capabilities, and data capabilities are very important.
Therefore, this project is especially suitable for guys
who need to put the algorithm online from 0 to 1.

算法并不只是离线train model,甚至可以说离线train model只是工作很小的一部分。

for guys who need to solve various practical problems in actual work

The project not only contains algorithm theory, algorithm derivation,
but also more engineering and data aspects.
Most of them are actual problems encountered in work,
which can provide you with reference ideas in practice.
Therefore, this project is especially suitable for guys
who need to solve various practical problems in actual combat.


CSDN address


The layout and stability of github is higher than that of csdn.
In the future, it will be prioritized to maintain the projects on github,
and csdn will also keep synchronized updates.


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