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JavaScript

Most people often think of JavaScript (JS) as just a programming language; however, JavaScript, as well as JavaScript frameworks, JavaScript code have multiple applications besides web applications. That includes mobile applications, desktop applications, backend development, and embedded systems.

Looking around, you might also discover that a growing number of developers are leveraging JavaScript frameworks to learn new machine learning (ML) applications. JS frameworks, like Node JS, are capable of developing and running various machine learning models and concepts. 

Learn more about Introduction to Python for Data Science

NodeJS - Programming language
                                                                                         JavaScript – Programming language – Data Science Dojo

 

Best NodeJS libraries and tools for machine learning

To help you understand better, let’s discuss some of the best NodeJS libraries and tools for machine learning.

 

1. BrainJS:

BrainJS is a fast-running JavaScript-written library for neural networking and machine learning. Developers can use this library in both NodeJS and the web browser. BrainJS offers various kinds of networks for various tasks. It is fast and easy to use as it performs computations with the help of GPU.

If GPU isn’t available, BrainJS falls back to pure JS and continues computation. It offers numerous implementations on a neural network and encourages developing and building these neural nets on the server side with NodeJS. That is a major reason why a development agency uses this library for the simple execution of their machine learning projects. 

Pros:

  • BrainJS helps create interesting functionality using fewer code lines and a reliable dataset.
  • The library can also operate on client-side JavaScript.
  • It’s a great library for quick development of a simple NN (Neural Network) wherein you can reap the benefits of accessing the wide variety of open-source libraries. 

Cons:

  • There is not much possibility for a softmax layer or other such structures.
  • It restricts the developer’s network architecture and only allows simple applications. 

Cracking captcha with neural networks is a good example of a machine learning application that uses BrainJS. 

 

2. TensorflowJS:

TensorflowJS is a hardware-accelerated open-sourced cross platform to develop and implement deep learning and machine learning models. The library makes it easy for you to utilize flexible APIs for developing models with the help of high-level layer API or low-level JS linear algebra. That is what makes TensorflowJS a popular library for every JavaScript project that is based on ML.

There are an array of guides and tutorials on this library on its official website. It even offers model converters for running the pre-existing Tensorflow models under JavaScript or in the web browser directly. The developers also get the option to convert default Tensorflow models into certain Python models.

Pros:

  • TensorflowJS can be implemented on several hardware machines, from computers to cellular devices with complicated setups
  • It offers quick updates, frequent new features, releases, and seamless performance
  • It has a better computational graph visualization

Cons:

  • TensorflowJS does not support Windows OS
  • It has no GPU support besides Nvidia

NodeJS: Pitch Prediction is one of the best use cases for TensorflowJS.

 

3. Synaptic:

Developed by MIT, Synaptic is another popular JavaScript-based library for machine learning. It is known for its pre-manufactured structure and general architecture-free algorithm. This feature makes it convenient for developers to train and build any kind of second or first-order neural net architecture.

Developers can use this library easily if they don’t know comprehensive details about machine learning techniques and neural networks. Synaptic also helps import and export ML models using JSON format. Besides, it comes with a few interesting pre-defined networks such as multi-layer perceptions, Hopfield networks, and LSTMs (long short-term memory networks).

Pros:

  • Synaptic can develop recurrent and second-order networks.
  • It features pre-defined networks.
  • There’s documentation available for layers, networks, neurons, architects, and trainers. 

Cons:

  • Synaptic isn’t maintained actively anymore.
  • It has a slow runtime compared to the other libraries. 

Painting a Picture and Solving an XOR are some of the common Synaptic use cases.

 

4. MLJS:

MLJS is a general-purpose, comprehensive JavaScript machine learning library that makes ML approachable for all target audiences. The library provides access to machine learning models and algorithms in web browsers. However, the developers who want to work with MLJS in the JS environment can add their dependencies. 

MLJS offers mission-critical and straightforward utilities and models for unsupervised and supervised issues. It’s an easy-to-use, open-source library that can handle memory management in ML algorithms and GPU-based mathematical operations. The library supports other routines, too, like hash tables, arrays, statistics, cross-validation, linear algebra, etc. 

Pros:

  • MLJS provides a routine for array manipulation, optimizations, and algebra
  • It facilitates BIT operations on hash tables, arrays, and sorting
  • MLJS extends support to cross-validation

Cons:

  • MLJS doesn’t offer default file system access in the host environment of the web browser
  • It has restricted hardware acceleration support

Naïve-Bayes Classification is a good example that uses utilities from the MLJS library.

 

5. NeuroJS:

NeuroJS is another good JavaScript-based library to develop and train deep learning models majorly used in creating chatbots and AI technologies. Several developers leverage NeuroJS to create and train ML models and implement them in NodeJS or the web application. 

A major advantage of the NeuroJS library is that it provides support for real-time classification, online learning, and classification of multi-label forms while developing machine learning projects. The simple and performance-driven nature of this library makes machine learning practical and accessible to those using it. 

Pros:

  • NeuroJS offers support for online learning and reinforcement learning
  • High-performance
  • It also supports the classification of multi-label forms

Cons:

  • NeuroJS does not support backpropagation and LSTM through time

A good example of NeuroJS being used along with React can be discovered here.

 

6. Stdlib:

Stdlib is a large JavaScript-based library used to create advanced mathematical models and ML libraries. Developers can also use this library to conduct graphics and plotting functionalities for data analysis and data visualization.

You can use this library to develop scalable, and modular APIs for other developers and yourself within minutes, sans having to tackle gateways, servers, domains, build SDKs, or write documentation.

Pros:

  • Stdlib offers robust, and rigorous statistical and mathematical functions
  • It comes with auto-generated documentation
  • The library offers easy-API access control and sharing

Cons:

  • Stdlib doesn’t support developing project builds that don’t feature runtime assertions.
  • It does not support computing inverse hyperbolic secant.

Main, mk-stack, and From the Farmer, are three companies that reportedly use Stdlib in their technology stack.

 

7. KerasJS:

KerasJS is a renowned neural network JavaScript library used to develop and prepare profound deep learning and machine learning models. The models developed using Keras are mostly run in a web application. However, to run the models, you can only use CPU mode for it. There won’t be any GPU acceleration.

Keras is known as a JavaScript alternative for AI (Artificial Intelligence) library. Besides, as Keras uses numerous frameworks for backend, it allows you to train the models in TensorFlow, CNTK, and a few other frameworks.

Pros:

  • Using Keras, models can be trained in any backend
  • It can exploit GPU support offered by the API of WebGL 3D designs
  • The library is capable of running Keras models in programs

Cons:

  • Keras is not that useful if you wish to create your own abstract layer for research purposes
  • It can only run in CPU mode

A few well-known scientific organizations, like CERN, and NASA, are using this library for their AI-related projects.

 

Wrapping up:

This article covers the top five NodeJS libraries you can leverage when exploring machine learning. JavaScript may not be that popular in machine learning and deep learning yet; however, the libraries listed in the article prove that it is not behind the times when it comes to progressing in the machine learning space.

Moreover, developers having and utilizing the correct libraries and tools for machine learning jobs can help them put up algorithms and solutions capable of tapping the various strengths of their machine learning project.

We hope this article helps you learn and use the different libraries listed above in your project. 

September 27, 2022

Over the years, the popularity of different programming languages has been increasing. This blog lists down some of the top & most useful programming languages for you to learn.

The use of programming languages ​​remains a popular way of earning money and the main tool for creating modern technologies. Even if you start studying some of these programming languages right now, you can be sure of high wages and rapid career growth.

The more programming languages ​​you know and use, the higher your status will be. For the development of one application/technology, several of them can be used at once. Since the inception of the first computers, more than 8,000 programming languages have been invented. There are basic ones that are used everywhere. It is impossible to single out the best one, each has advantages and disadvantages.

Below is the list to get you acquainted with the best programming languages ​​and find out which one to start with:

Python 

Python is a simple programming language that is suitable for beginners and will be a relatively easy way to get into a new profession. A clear code, a large library of tools, and a minimum of tricks allow you to quickly get the hang of it, making the language the most popular in education and also helping you to learn data science. Although learning Python for Data Science is not an easy task, there is a lot of training out there that can help one get started.  It is not for nothing called “language with batteries included”, it itself provides methods for solving basic problems. It is easy to integrate with C and C ++ languages.

Python’s performance is inferior to other languages, but because of this, it does not lose its relevance. Scientists all over the world use it for machine learning. Plus, it’s ideal for web services; backend, and sysadmin.

C & C++

The C language appeared in 1975, and its more multifaceted extension C ++, in 1985. They are the progenitors of most programming languages. Every 3 years the C ++ language is updated, and today there is already the 20th ISO standard. Initially, the C language was developed for less powerful computers, was economical, and more tied to the hardware. This binding remains today, which allows you to “squeeze” the maximum out of productivity. Now the language is used both for game development and for machines with a low-power processor.

These complex languages ​​are not the most fun places to learn programming. When studying, you can quickly burn out and say goodbye to the profession. However, it is C ++ that will help to fully probe the “brain” of the computer, which is extremely important for the programmer. This hard start is suitable for those who want to understand the basics. C ++ does not support validation at the time of writing the code, which also complicates the development work.

That is why these specialists are in great demand.

.NET

.NET is a framework from Microsoft that allows you to use the same namespaces, libraries, and APIs for different languages. .NET supports several languages: along with C#, it also includes VB.NET, C ++, F #, as well as various dialects of other languages ​​tied to .NET.

.NET is fairly widespread in the development of in-house software products, but it is still relatively rare in web development, like other software products from Microsoft. Therefore, finding .NET developers for a web project can be quite difficult. The use of .NET usually  “pulls” the purchase of other software from Microsoft. However, if you are looking for a promising direction, this is a great option.

JavaScript

Created in 1995, JavaScript is the dominant frontend language around the world. Its relevance is not lost even for a minute, it can be used to create interactive websites. It is also easy to learn, but today it is not enough for development, and the number and quality of frameworks can become difficult. That is why you should not start with it, because constant retraining for the changing frontend, by virtue of already active programmers.

Thanks to a large number of add-ons, the functionality of JavaScript is limitless. The main disadvantage is that due to the fact that the language is used to encode pop-ups, we often have to deal with malicious content.

Java

Java was also introduced in 1995 but has nothing to do with JavaScript. It is in demand in the backend and occupies a good position in it. Death is predicted for the language every year, but it seems that this will not happen soon. Behind the “boring and verbose” structure, many find the perfect solution to many problems. For example, it is used by banking structures to write mobile applications for large companies.

The main advantage is that the developed application will run on any platform that supports Java due to the weak link system. That is, after the initial creation, there is no need to modify the application specifically for each server.

The disadvantages of the language include additional payment for the licensed version of the Java Development Kit, and it is not suitable for applications in the cloud.

Learning Java first is not worth it, it is the perfect complement to other more fundamental languages.

Swift

Created by Apple in 2014, Swift has grown exponentially in popularity. The creators positioned it as a replacement for Objective-C and the beginning of a “new era” of programming. But so far it is in demand for only iOS applications.

The language is perfectly adapted for both custom and server-side development. The syntax is easy to read and the code runs quickly.

It’s only worth learning if you’re going to develop apps for Apple products.

But even for iOS, it does not always work. Since the language is new, it is used to write applications for at least the seventh generation of iOS. In addition, Swift still has many shortcomings, it is unstable and has a small number of third-party resources to work with.

Conclusion

A number of programmers build their careers with professional knowledge in only one programming language, but they are more proficient in several of them at once, which significantly increases their chances of succeeding in their careers. It is difficult to say which of them should be studied. For example, if you want to work in a large company, it is better to learn C and Java, Python and JavaScript are suitable for participation in web startups, and for iOS mobile applications, it is enough to have knowledge of Swift.

June 10, 2022

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