TensorFlow.js brings machine learning to JavaScript

Increasingly in AI, developers want to do more powerful things with browsers, such as speech recognition; image and object recognition; and pattern and anomaly detection. TensorFlow.js aims to put that power in the browser form factor without the need for additional cloud resources or specialized server or chipsets. "This means that even casual app developers looking to add machine learning capabilities to their web-based apps or even mobile apps that leverage JavaScript- or Node.js-based server apps can use TensorFlow.js to add that capability," said Ronald Schmelzer, an analyst at Cognilytica, in Ellicott City, Md. TensorFlow.js runs machine learning models entirely in the browser, using JavaScript and high-level layers API. As TensorFlow presides as the standard library for building machine learning models these days, TensorFlow.js enables JavaScript developers to reuse TensorFlow skills, extensions and models, and will enable more standardization across the field as a whole, said Adam Smith, CEO of San Francisco-based Kite, which uses machine learning to help developers write code.