Advantages and challenges of JavaScript

javascript Aug 16, 2019

Despite my optimism towards the future of ML in JavaScript, most developers today would still choose Python for their new projects, and nearly all large-scale production systems are developed in Python or other languages more typical to ML.

JavaScript, like any other tool, has its advantages and disadvantages. Much of the historic criticism of JavaScript has focused on a few common themes: strange behavior in type coercion, the prototypical object-oriented model, difficulty organizing large codebases, and managing deeply nested asynchronous function calls with what many developers call callback hell. Fortunately, most of these historic gripes have been resolved by the introduction of ES6, that is, ECMAScript 2015, a recent update to the JavaScript syntax.

Despite the recent language improvements, most developers would still advise against using JavaScript for ML for one reason: the ecosystem. The Python ecosystem for ML is so mature and rich that it's difficult to justify choosing any other ecosystem. But this logic is self-fulfilling and self-defeating; we need brave individuals to take the leap and work on real ML problems if we want JavaScript's ecosystem to mature. Fortunately, JavaScript has been the most popular programming language on GitHub for a few years running, and is growing in popularity by almost every metric.

There are some advantages to using JavaScript for ML. Its popularity is one; while ML in JavaScript is not very popular at the moment, the language itself is. As demand for ML applications rises, and as hardware becomes faster and cheaper, it's only natural for ML to become more prevalent in the JavaScript world. There are tons of resources available for learning JavaScript in general, maintaining Node.js servers, and deploying JavaScript applications. The Node Package Manager (npm) ecosystem is also large and still growing, and while there aren't many very mature ML packages available, there are a number of well built, useful tools out there that will come to maturity soon.

Another advantage to using JavaScript is the universality of the language. The modern web browser is essentially a portable application platform which allows you to run your code, basically without modification, on nearly any device. Tools like electron (while considered by many to be bloated) allow developers to quickly develop and deploy downloadable desktop applications to any operating system. Node.js lets you run your code in a server environment. React Native brings your JavaScript code to the native mobile application environment, and may eventually allow you to develop desktop applications as well. JavaScript is no longer confined to just dynamic web interactions, it's now a general-purpose, cross-platform programming language.

Finally, using JavaScript makes ML accessible to web and frontend developers, a group that historically has been left out of the ML discussion. Server-side applications are typically preferred for ML tools, since the servers are where the computing power is. That fact has historically made it difficult for web developers to get into the ML game, but as hardware improves, even complex ML models can be run on the client, whether it's the desktop or the mobile browser.

If web developers, frontend developers, and JavaScript developers all start learning about ML today, that same community will be in a position to improve the ML tools available to us all tomorrow. If we take these technologies and democratize them, expose as many people as possible to the concepts behind ML, we will ultimately elevate the community and seed the next generation of ML researchers.

TypeScript language

The development and sharing of new packages on npm was not the only result of JavaScript's popularity. JavaScript's increasing usage as a primary programming language caused many developers to lament the lack of IDE and language tooling support. Historically, IDEs were more popular with developers of compiled and statically-typed languages such as C and Java, as it’s easier to parse and statically analyze those types of languages. It wasn't until recently that great IDEs started appearing for languages such as JavaScript and PHP, while Java has had IDEs geared towards it for many years.

Microsoft wanted better tooling and support for their large-scale JavaScript projects, but there were a few issues with the JavaScript language itself that got in the way. In particular, JavaScript's dynamic typing (the fact that var number could start its life as the integer 5, but then be assigned to an object later) precludes using static analysis tools to ensure type safety, and also makes it difficult for an IDE to find the correct variable or object to autocomplete with. Additionally, Microsoft wanted a class-based object-oriented paradigm with interfaces and contracts, but JavaScript's object-oriented programming paradigm was based on prototypes, not classes.

Microsoft therefore invented the TypeScript language in order to support large-scale JavaScript development efforts. TypeScript introduced classes, interfaces, and static typing to the language. Unlike Google's Dart, Microsoft made sure TypeScript would always be a strict superset of JavaScript, meaning that all valid JavaScript is also valid TypeScript. The TypeScript compiler does static type checking at compile time, helping developers catch errors early. Support for static typing also helps IDEs interpret code more accurately, making for a nicer developer experience.

Several of TypeScript's early improvements to the JavaScript language have been made irrelevant by ECMAScript 2015, or what we call ES6. For instance, TypeScript's module loader, class syntax, and arrow function syntax have been subsumed by ES6, and TypeScript now simply uses the ES6 versions of those constructs; however, TypeScript still brings static typing to JavaScript, which ES6 wasn't able to accomplish.

I bring up TypeScript here because, while we won't be using TypeScript in the examples in this book, some of the examples of ML libraries we examine here are written in TypeScript.

For instance, one example found on the deeplearn.js tutorials page shows code that looks like the following:

const graph = new Graph();
 // Make a new input in the graph, called 'x', with shape [] (a Scalar).
 const x: Tensor = graph.placeholder('x', []);
 // Make new variables in the graph, 'a', 'b', 'c' with shape [] and   
 // initial values.
 const a: Tensor = graph.variable('a',;
 const b: Tensor = graph.variable('b',;
 const c: Tensor = graph.variable('c',

The syntax looks like ES6 JavaScript except for the new colon notation seen in const x: Tensor = … : this code is telling the TypeScript compiler that the const x must be an instance of the Tensor class. When TypeScript compiles this code, it first checks that everywhere x is used expects a Tensor (it will throw an error if not), and then it simply discards the type information when compiling to JavaScript. Converting the preceding TypeScript code to JavaScript is as simple as removing the colon and the Tensor keyword from the variable definition.

You are welcome to use TypeScript in your own examples as you follow along with this book, however, you will have to update the build process that we set up later to support TypeScript.

Neeraj Dana

Experienced Software Engineer with a demonstrated history of working in the information technology and services industry. Skilled in Angular, React, React-Native, Vue js, Machine Learning