TensorFlow.js is a JavaScript (JS) library that is used specifically for machine learning (ML). It is used for training and deploying machine learning models in your web browser and Node.js.
JavaScript machine learning is becoming increasingly popular because of its wide usage across sectors.
Simply put, machine learning can be defined as the use and development of algorithms for computer systems that can learn and adapt without supervision. First, the data is captured through various programs. Then, statistical models are generated for analysis and inferences are drawn from data patterns.
Machine learning is an important component of artificial intelligence (AI). You can perform machine learning with Java, Python, or JavaScript. Whatever be your choice, each programming language has its way of training and deploying.
Before you dive deeper into JavaScript machine learning, you need to know about a few terms and concepts of artificial intelligence and machine learning. As we move forward, we shall be using these terms again and again. So, it is better if you get acquainted with them early on when planning to learn machine learning.
Why you Should use TensorFlow.js?
If you are wondering why you should use TensorFlow Js, then there are many advantages of building ML apps using tfjs file:
We have mentioned repeatedly that TensorFlow.js is light to use. Hence, that makes it the perfect choice for browser engines and consoles to use this JavaScript library. TensorFlow Js can be used on any device, be it a PC, mobile, or tablet.
There are a number of ML apps that incorporate TensorFlow Js in their backend, so obviously, there is no other way around here. Further, JavaScript can be used both on the front end and the backend. So, TensorFlow Js can be seamlessly integrated with the app architecture. Moreover, the speed of the app is not substantially affected.
Another point that we would like to mention is that there are many JavaScript developers in the software industry. Thanks to the boom in UI/UX start-ups! TensorFlow Js is easy to pick up and it will be a cakewalk for JavaScript developers. You can also use basic JavaScript for making ML apps using TFJS once you get the hang of it.
Remember that you have made a wise decision whenever you plan to use JavaScript for machine learning. In the future, all browser-based apps which use JS shall incorporate TensorFlow Js for machine learning.
There are many ML projects which have used TensorFlow Js for their completion. One of the most famous ones is face recognition software. This piece of software allows you to recognize faces and emotions. It also lets you find out the gender. You can also find and verify landmarks with it.
Other TensorFlow Js projects include image editing apps where the style of images could be transferred. This software can also help to manipulate images by selectively blurring unsafe pictures while displaying safe ones.
You can also generate graphs from raw data with the help of TensorFlow Js. The list of possibilities for using TensorFlow Js is endless.
There are two ways in which ML apps can be deployed using TensorFlow Js:
1. By using the browser – In the browser, you can launch the ML app through Heroku, Github, or Firebase. A paid option would be Digital Ocean. These hosting service providers have the necessary support to back up ML apps. There are many tutorials over the web that will explain to you how to deploy the app. Small ML apps can be deployed for free.
2. By using Node.js – After installing Node.js on your local server, you can deploy the ML app. Your app will get hosted on the 8080 port. The URL will look something like this - http://localhost:8080/xxxx. The method of deployment can be seen through a tutorial video.
There are many TensorFlow.js tutorials on the web. Pick the best TensorFlow Js tutorial or join a course to learn the ML deployment procedure in detail.
So far we’ve take a look at building apps for machine learning with Java and how TensorFlow.js can be an integral part of it.
To learn how you can make use of TensorFlow.js and other tools to build ML apps and models, check out Hero Vired’s Integrated Program in Data Science, Machine Learning, and Artificial Intelligence to get a jumpstart in your career and realize your dream of becoming a machine learning developer.
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