Mapreduce Machine Learning

MapReduce: Simple data processing on large cluster

Earlier we used HDFS for related operations, and we also understood the principles and mechanisms of HDFS. With a distributed file system, how do we handle files? This is the second component of Hadoop-MapReduce.

C + + computer vision ccv-based on C language/provides cache/core machine Vision Library, novel Machine Vision Library opencv-It provides C + +, C, Python, Java and MATLAB interfaces, and supports Windows, Linux, Android and Mac OS operating system. General machine learning Mlpack dlib Ecogg Shark Closure Universal machine learning Closure Toolbox-cloj ...

Over the past two years, the Hadoop community has made a lot of improvements to mapreduce, but the key improvements have been in the code layer, http://www.aliyun.com/zixun/aggregation/13383.html "> 　　Spark, as a substitute for MapReduce, has developed very quickly, with more than 100 contributors from 25 countries, and the community is very active and may replace MapReduce in the future. The high latency of mapreduce has become ha ...

In Google data centers there are large numbers of data to be processed, such as a lot of Web pages crawled by web crawlers (WebCrawler). 　　 　　Since many of these data are PB levels, the process has to be as parallel as possible, and Google has introduced the MapReduce distributed processing framework to address this problem. The technology overview MapReduce itself originates from functional languages, mainly through "map" and "Reduce" ...

The Apache Software Foundation has officially announced that Spark's first production release is ready, and this analytics software can greatly speed up operations on the Hadoop data-processing platform. 　　As a software project with the reputation of a "Hadoop Swiss Army Knife", Apache Spark can help users create performance-efficient data analysis operations that are faster than they would otherwise have been on standard Apache Hadoop mapreduce. Replace MapReduce ...

Spam filtering, face recognition, recommendation engine-when you have a large dataset and want to use them to perform predictive analysis and pattern recognition, machine learning is the only way. In this science, computers can learn, analyze and manipulate data independently without prior planning, and more and more developers are now concerned with machine learning. The rise of machine learning technology is also important not only because hardware costs are getting cheaper and more powerful, but free software surges that machine learning is easily deployed on stand-alone or large-scale clusters The diversity of machine learning libraries means that whatever language you like ...

This paper is an excerpt from the book "The Authoritative Guide to Hadoop", published by Tsinghua University Press, which is the author of Tom White, the School of Data Science and engineering, East China Normal University. This book begins with the origins of Hadoop, and integrates theory and practice to introduce Hadoop as an ideal tool for high-performance processing of massive datasets. The book consists of 16 chapters, 3 appendices, covering topics including: Haddoop;mapreduce;hadoop Distributed file system; Hadoop I/O, MapReduce application Open ...

Text processing in the MapReduce technology Lirui, Wang bin for text processing a lot of data sets have reached TB, PB or even larger, traditional stand-alone method is difficult to effectively deal with these data. In recent years, the MapReduce computing framework has been widely accepted and used by both academia and industry, which can solve the problem of parallel processing of large-scale data in concise form and distributed scheme. At present, MapReduce has been used in natural language processing, machine learning and large-scale map processing and other fields. This article first to Mapre ...

In the past few years, the use of Apache Spark has increased at an alarming rate, usually as a successor to the MapReduce, which can support thousands of-node-scale cluster deployments. In the memory data processing, the Apache spark is more efficient than the mapreduce has been widely recognized, but when the amount of data is far beyond memory capacity, we also hear some organizations in the spark use of trouble. Therefore, with the spark community, we put a lot of energy to do spark stability, scalability, performance, etc...