R Machine Learning Packages

Which of the following 5 languages are NODE, LUA, Python, Ruby, R, and which will be better applied in the 2014? I don't hesitate to choose R. 　　R is not only 2014, but also the protagonist for a longer period of time. 1. My programming background programmer, Architect, from the beginning of programming to today, has been convinced that Java is the language to change the world, Java has done, and has been very brilliant. But when the world of Java is becoming bigger and larger, when it becomes omnipotent, it is not professional enough for other languages to develop ...

R is a GNU open Source Tool, with S-language pedigree, skilled in statistical computing and statistical charting. An open source project launched by Revolution Analytics Rhadoop the R language with Hadoop, which is a good place to play R language expertise. The vast number of R language enthusiasts with powerful tools Rhadoop, can be in the field of large data, which is undoubtedly a good news for R language programmers. The author gave a detailed explanation of R language and Hadoop from a programmer's point of view. The following is the original: Preface wrote several ...

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 ...

With large data being adopted by more enterprises, the compilation and production language of data processing and analysis algorithms have been widely concerned. and unknowingly, open source statistics language R has become a basic technology for large data scientists and developers. 　　In all programming languages and techniques, popularity has soared. The following is the translation through the integration with the large data processing tools, R provides the depth statistical capability of large datasets, including statistical analysis and data-driven visualization. In industries such as finance, pharmaceuticals, media, and sales, which can directly take decisions from data, R has been applied in depth. ...

Machine learning engineers are part of the team that develops products and builds algorithms and ensures that they work reliably, quickly, and on a scale.

With the growing interest in large data analysis, software vendors Revolution http://www.aliyun.com/zixun/aggregation/16353.html "> 　　Analytics has improved its flagship R-language statistics feature to enable it to run with the Hadoop data processing platform. This new revolution R Enterprise 7 (RRE 7) also enables R in the Teradata Database ...

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 ...

With the upsurge of large data, there are flood-like information in almost every field, and it is far from satisfying to do data processing in the face of thousands of users ' browsing records and recording behavior data. But if only some of the operational software to analyze, but not how to use logical data analysis, it is also a simple data processing. Rather than being able to go deep into the core of the planning strategy. Of course, basic skills is the most important link, want to become data scientists, for these procedures you should have some understanding: ...

With the upsurge of large data, there are flood-like information in almost every field, and it is far from satisfying to do data processing in the face of thousands of users ' browsing records and recording behavior data. But if only some of the operational software to analyze, but not how to use logical data analysis, it is also a simple data processing. Rather than being able to go deep into the core of the planning strategy. Of course, basic skills is the most important link, want to become data scientists, for these procedures you should have some understanding: ...

With the upsurge of large data, there are flood-like information in almost every field, and it is far from satisfying to do data processing in the face of thousands of users ' browsing records and recording behavior data. 　　But if only some of the operational software to analyze, but not how to use logical data analysis, it is also a simple data processing. 　　Rather than being able to go deep into the core of the planning strategy. Of course, basic skills is the most important link, want to become data scientists, for these procedures you should have some understanding ...