In the past few years, Python has been the preferred and primary language in machine learning and deep learning. Most of the tutorials in this field only offer Python or, in parallel, the R programming language. The reasons for Python’s popularity include having a complete list of machine learning and deep learning libraries, optimal execution of operations, scalability, and diverse features.
But is Python the only option for machine learning and programming machine learning applications?
No! Many developers and professionals in this field are already using JavaScript. Why? For 4 reasons that you will read in this content.
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Machine learning with JavaScript
JavaScript is a programming language used to create websites with dynamic content. Everything that moves on web pages, refreshes, or changes status without reloading the page, is created or controlled with the help of JavaScript. Features such as animated graphics, photo slideshow, auto-completion of text in the field, and auto-updating of some pages are examples of this.
In the machine learning space, JavaScript has not (so far) replaced Python. But there are good reasons to use JavaScript for machine learning. So, it is possible to learn JavaScript with this goal. In the following, we will discuss four reasons to learn JavaScript for machine learning.
1. Private Machine Learning
Most machine-learning applications rely on client/server architectures. The client/server architecture has many advantages. Developers can run their models on servers and make them available to application users through web APIs. And in this way, use large neural networks that do not run on application devices.
But in many cases, it is preferable to perform machine learning inference on the user’s device. For example, for privacy reasons, users may not want to send their photos, private messages, and emails to a server running a machine-learning model.
Fortunately, not all machine learning applications require expensive servers. Many models can be squeezed into user devices, and mobile phone manufacturers are equipping their devices with chips that can support local deep-learning inference mechanisms.
The problem is that machine learning with Python is not supported on many devices. Python is installed by default on MacOS and most versions of Linux. But the user must install the machine learning libraries separately. Windows users also have to install Python manually, and mobile operating systems have very poor support for Python interpreters.
But JavaScript!
JavaScript is supported by all modern mobile and desktop browsers. This means that JavaScript machine learning programs are guaranteed to run on most desktop and mobile devices. Therefore, if your machine learning model runs in JavaScript code in the browser, you can be sure that it will be accessible to almost all users.
There are already several machine learning libraries with JavaScript. For example, TensorFlow.js is the JavaScript version of Google’s famous machine learning and deep learning library TensorFlow. If you go to the TensorFlow.js home screen on your smartphone, tablet, or desktop computer, you’ll find plenty of ready-made examples for using machine learning with JavaScript. Machine learning models run on your device without sending any data to the cloud and require no additional software to be installed. Other powerful JavaScript machine-learning libraries include ML5.js, Synaptic, and Brain.js.
2. Fast and customized ML models
Privacy isn’t the only benefit of machine learning with JavaScript. In some applications, sending data back and forth from the device to the server can cause delays that make the user experience negative. Also, users may want to run their machine learning models even when they don’t have an internet connection. In these cases, it is very useful to have machine learning models with JavaScript running on the user’s device.
Another important application of machine learning with JavaScript is model customization. For example, suppose you want to use machine learning to create a text generation model that adapts to the user’s preferred language. A solution could be to store a model for each user on the server that only focuses on his information. As the number of users increases in this way, additional load is placed on your server and requires you to store more sensitive data in the cloud.
An alternative approach is to create a base model on the server and a copy of it on the user’s device, and fine-tune the model according to the user’s data with the help of JavaScript machine learning libraries.
This, on the one hand, keeps the data on the users’ devices and removes the need to send them to the server. On the other hand, it frees up server resources by avoiding sending a load of inference and additional training to the cloud memory. Users can still use machine learning features even when they are disconnected from your servers.
3. Integration of machine learning in web and mobile applications
Another advantage of machine learning with JavaScript is its compatibility with mobile applications. Python support on mobile operating systems is still in its infancy. But now there is a rich collection of cross-platform application development tools with JavaScript, such as Cordova and Ionic. These tools have become very popular because they allow you to write your code just once and then deploy it to both iOS and Android.
To make the code compatible with different operating systems, cross-platform development tools provide a review: a browser object that can execute JavaScript code and can be embedded in a native application from the target operating system. These browser objects support machine learning libraries with JavaScript.
If you’ve written your mobile app in native code and want to integrate your JavaScript machine-learning code, you can add your own embedded browser object (like WKWebView on iOS) to your app.
Several other machine-learning libraries with JavaScript
There are other machine learning libraries for mobile apps, such as TensorFlow Lite and Core ML. Of course, they require native coding on the mobile platform you’re developing your app for. It’s also possible that if you’ve already implemented a browser version of your machine learning app, you can easily port it to your mobile app with little or no changes.
4. Machine learning with JavaScript on the server
One of the main challenges of machine learning is training models. Especially in deep learning, where learning requires a lot of computation. Deep learning models can be trained on user devices. Of course, if the neural network is large, this process may take weeks or months.
Machine learning with server-side JavaScript is also growing. You can run JavaScript machine learning libraries on Node.js (a JavaScript application server engine). TensorFlow.js has a special version that is suitable for servers running Node.js. The JavaScript code you use to interact with TensorFlow.js is the same code you use for applications running in the browser.
But behind the scenes, the library uses your server’s special hardware to speed up training and inference. Machine learning with Node.js is relatively new, but developing rapidly. Because there is a growing interest in adding machine learning capabilities to web and mobile applications.