5 ways to take machine learning to Java, Python, and go programming languages

Source: Internet
Author: User
Keywords Java large data machine learning PYTHON open source JavaScript

The "Editor's note" machine learning seems to have turned from obscurity to the limelight overnight, as well as more open source tools for machine learning, but the challenge now is how to get developers interested in machine learning and the data they are prepared to use to actually use them, This paper collects the common and practical open source machine learning tools in several languages, which is worth paying attention to, which is from InfoWorld.

The following is the original text:

After decades of development as a professional discipline, machine learning seems to be the most high-profile business tool in the night. The challenge now is how to make it work, especially for developers and the data scientists who are preparing to use it.

To this end, we have collected some of the most common and practical open source machine learning tools, through this article to share.


Data scientists have embraced python in the hope of another more open alternative to the R language, where many employers now seek large data specialists, and Python is the necessary skill. As a result, a large number of machine learning software libraries are starting to appear in the ever-expanding list of Python software.

The first recommendation is Scikit-learn, which can be loaded into algorithms and modules, is widely appreciated in GitHub (the number of fork versions is close to 2000) and favored by many technology giants. The other, followed by Pybrain, is designed to reduce the difficulty of use and to provide the ability to connect with other powerful tools. As the name suggests, Pybrain's focus is on neural networks and unsupervised learning, and it also provides a set of mechanisms for training and redefining algorithms.


Google's system language, due to its concurrent design, makes it seem like an ideal environment for writing machine learning libraries. While the associated library project is still small in size, there are some notable golearn that its developers describe as a "built-in battery" machine learning library. It provides filtering, classification, and regression analysis, and many other tools. Another set of smaller and more basic libraries is MLGO, which currently offers a very small number of algorithms, but plans to launch more in the future.

Javaon Hadoop

Mahout (meaning "Elephant Knights" in Hindi) contains several common machine learning methods in the context of popular large data frames. This package is built around algorithms, not methods, so you need to have some kind of algorithmic base, in other words, if you're serious, it's not hard to see how each part of the function is integrated, for example, you can build a user based recommendation system from a few lines of code.

Another Hadoop based machine learning Project is the Oryx of the Cloudera company, which is characterized by further analysis of mahout processing results by delivering real-time streaming results rather than processing batch operations. The project is still in its infancy, and note that it is only a project and not a real product, but it is improving, so it is worth paying attention to.


In addition to the mahout, which is primarily for Hadoop, other Java-oriented machine learning libraries are also widely used. Weka is a desktop application developed by the New Zealand University of Waikato, which adds visualization and data mining capabilities to the common algorithm set. For those who want to create a front-end for their work or plan to use Java as their initial development, Weka may be the best choice. JAVA-ML is fine, but it's better for developers who are accustomed to working with Java and machine learning.


Jokes about JavaScript ("Atwood's Law"), the original intention is that any content that can be written by JavaScript will eventually be written by JavaScript, which applies to the Machine learning library as well. The number of scenarios currently written by JavaScript is still relatively small in this area, and most options are just a single algorithm rather than a complete library, but some useful tools have come to the fore. Convnetjs allows you to conduct in-depth neural network training directly in the browser, while the name Brain provides the neural network as an installable NPM module. In addition, the ENCOG library is also a concern, and it works on a variety of platforms: Java, C #, C + +, and JavaScript.

Original link: 5 ways to add machine learning to Java, JavaScript, and more (Compile/Wei revisers/Zhonghao)

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