Machine Learning In JavaScript

The web is ubiquitous, yet many machine learning algoriths cannot be readily found and applied in JavaScript. An inherent downside to machine learning in JavaScript is lack of speed. However, as the language becomes increasingly more popular, the need for machine learning algorithms steadily rises. First and foremost, JavaScript is the language of the web browser. Having machine learning available in the web browser allows for delivery of machine learning tools to users in the most convenient way possible. This opens up opportunities for non-Software Developers to gain access to the power of machine learning. In addition, browser-based machine learning allows for effective visualization of algorithms, which can help with education, as well as quick visualization of data. Second, with the rise of Node.js, JavaScript is being increasingly relied upon to do non-Browser Based work. The growth of the Node.js ecosystem will rely on the availability of easy to use libraries. A final reason for implementing machine learning in JavaScript is Atwood’s law: “any application that can be written in JavaScript, will eventually be written in JavaScript” [1].