Nine top JavaScript machine learning frameworks for 2019

Intelligent software is becoming more and more accessible to all businesses regardless of the size or industry. Applying smart algorithms to routine tasks, you have a chance to boost the productivity of your company by 40%, improve the customer experience, and use the data more efficiently. As true JS fans, we are happy to say, that JavaScript machine learning tools start playing a substantial role in the penetration of 'digital brains' into our daily lives.

Everybody who is slightly familiar with the subject knows that Python and R have been primary choices for machine learning projects for years. However, things are changing. According to the GitHub review of best machine learning technologies, JavaScript occupies the third position after Python and C++, while R falls in the eighth place.  

best programming languages for mahine learning

We must admit that compared to JavaScript Python has the more advanced and rich ML environment. It comes as no surprise that 83% of data professionals use it on a regular basis. However, JavaScript also has some advantages including the following:

  • Versatility. JavaScript opens the way to full-stack and cross-platform development so programmers can use one language to perform a wide range of tasks. With JS, you have widely used Angular and React for front-end design while Node.js frameworks allow you to build a robust and scalable back-end part. Another JavaScript popular JavaScript framework, React Native enables web programmers to build trending mobile apps for iOS and Android.:
  • Popularity. According to the Stack Overflow's 2019 survey, JavaScript is the most popular technology used by 69.7% of professional developers. With the growing demand for smart apps and increasing speed of hardware, machine learning algorithms in the JS world are also evolving rapidly, benefiting from the support of an active dev community.

The number of machine learning projects in JavaScript is constantly growing and their capabilities are progressing accordingly.  However, prior to discussing JavaScript frameworks for machine learning in more detail, we need to mention some important concepts you will come across when dealing with smart systems.

What is machine learning and how does it differ from deep learning?

Artificial intelligence (AI) is defined by Wikipedia as 'intelligence demonstrated by machines'. In other words, it is the ability of computer systems to emulate human behavior and thought..

Machine learning (ML) is a sub-type of artificial intelligence, based on the concept that people can teach machines to analyze data and find solutions to daily problems, relying on patterns and previous experience rather than external instruction. This concept has been present for decades. First coined in 1959, the phrase 'machine learning' made a long journey from a mere idea to practical application in modern society. It is found in virtually all aspects of life, including automobile technologies, TV recommendations, personalized shopping, and more.

Deep learning is the most advanced branch of machine learning. Its main task is to closely mimic the way a human brain works, or more precisely, how scientists believe our brain works.  Deep learning systems can handle significant amounts of information thanks to utilizing multi-layered artificial neural networks. These 'deep' processing nets are able to extract different patterns and details until the system recognizes the object it is looking for.

Artificial neural networks (ANNs) are complex systems designed to mimic neuron connections in the human brain. All of the latest breakthroughs in image and speech recognition, as well as natural language processing, would be impossible without ANNs.

machine learning vs deep learning

Image source: Medium

What is the best JavaScript machine learning framework?

The answer to this question depends on your particular goals, the previous experience of your development team, and several other factors. Based on our experience with different JS frameworks for machine learning, we have chosen nine technologies you should definitely try in 2019. The following libraries are arranged in alphabetical order with disregard to popularity or capabilities.

# 1 Brain.js

Brain.js is a JavaScript machine learning library which facilitates designing, training and running neural networks in any browser or on the server-side with Node.js. It works with a variety of networks for different purposes including:

  • Feed-forward neural networks where data always moves in one direction and never flows backward
  • Recurrent neural networks (RNNs) which can store relevant pieces of information and use them in context to understand and classify data
  • Long short-term memory networks (LSTMs), or type of RNNs, built to learn long-term dependencies. These advanced algorithms power Apple's Siri, Amazon's Alexa, and Google's speech recognition and translation technologies. LSTM is the most profitable achievement in AI, applied to everything from medical diagnostics to musical composition.

recurrent neural networks structure

Image credit:

#2 ConvNetJS

This library was created at Stanford University and became very popular on GitHub with many contributing devs. ConvNetJS is no longer actively maintained, yet it still remains one of the most advanced tools to execute deep learning models in browser or Node.js.

ConvNetJS supports:

  • Common neural network architectures
  • Convolutional neural networks (CNN) widely used for the analysis of images
  • Classification and regression cost functions
  • Reinforcement learning module based on deep Q-learning

ConvNetJS performs different AI tasks including pattern recognition. It also helps with JavaScript debugging and data encryption. ConvNetJS is easy to implement, though it requires some experience in neural networks and works a little slower than similar tools.

#3 DeepForge

DeepForge is not a library, rather a user-friendly environment for creating deep learning models. It provides you with an intuitive interface to quickly build, train, and iterate neural network models from within the Chrome browser. Other browsers are not officially supported, and in order to run the project, you are required to install both Node.js and MongoDB.

deepforge machine learning environment

#4 Keras.js

Keras is a leading neural network library for the creation and training of deep learning models across a vast range of platforms. Written in Python and boasting over 250,000 individual users, it is the second most popular deep learning framework after TensorFlow (in 2017, TensorFlow integrated the Keras API into the platform's core).

Such tech heavyweights as Uber, Netflix, and Yelp applied Keras models to increase functionality. The library is also popular among large scientific organizations such as CERN and NASA. At the same time, it is widely used by startups as a means of incorporating digital intelligence into their business processes.

A Javascript version of the artificial intelligence library, Keras.js allows you to run Keras models inside the client's browser and take advantage of the GPU (Graphics Processing Unit) support provided by WebGL 3d-graphics API. It should be noted that Keras.js goes well with Node.js, but only in the CPU (Central Processing Unit) mode.

#5 Mind

Mind can be installed both on the client side or under Node.js. The library permits you to build machine learning models for making accurate predictions. Its creator Steven Miller took the trouble of writing a tutorial on how to build neural networks. Also, there is a demo that shows how Mind copes with predicting the kind of movies you like.

#6 STDLib

STDLib is a huge library for the development of scientific and mathematical apps. Besides building advanced statistical models, STDLib gives you a wide range of tools and methods to visualize information, perform exploratory data analysis, and more. The long list of its benefits includes:

  • a wide collection of probability distributions (over 35) and mathematical functions (over 150).
  • general tools for functional programming and asynchronous control flow.
  • over 200 tools for data validation and feature detection.
  • a plot API for data analysis and visualization.

#7 Synaptic

An actively maintained machine learning JavaScript project, Synaptic lets you work with various neural networks in the browser or within Node.js. The architecture-free library contains several pre-built structures that allow you to quickly test and compare multiple algorithms such as:

  • multilayer perceptrons (a type of feed-forward neural networks).
  • long short-term memory, or LSTM (a type of recurrent neural networks).
  • liquid state machines, or LSM (a type of spiking neural networks that more accurately simulate the work of real biological neurons).
  • Hopfield networks  (a type of recurrent neural networks).

You can take advantage of the detailed guide into JavaScript neural network design written by the Synaptic team, other guides, tutorials, and articles as well as practical demos.

#8 TensorFlow.js

Developed by Google Brain team, TensorFlow forms the basis for advanced neural network software like DeepDream, which can capture, detect, and classify images, and even generate natural-language captions for them. This free end-to-end platform consists of multiple tools, libraries, and other resources that let a developer build applications on top of deep neural networks. Originally, TensorFlow has a Python programming interface, with its core written in highly optimized C++.

In 2018, Google released TensorFlow.js, a top JavaScript machine learning framework previously called DeepLearn.js. It allows programmers to import existing ML models they have previously trained offline, re-train them or build new models from the ground up and deploy them either with Node.js or on the client side.

machine learning DeepDream image

An image of three men in a pool processed by DeepDream. Image source: Wikipedia

#9 WebDNN

Designed in Japan, this library serves to execute deep neural network models in web browsers and do it fast. What is the secret of better performance? First, WebDNN optimizes models and compresses data. To run models on GPU, it utilizes WebGPU, a next-generation JavaScript API for a quick rendering of 3D graphics. In addition, the framework speeds up CPU execution with the help of WebAssembly, which compiles the code written in high-level programming languages into small lightweight modules.

training machine learning models

Smarter software for more revenue

Machine learning frameworks drastically simplify creating intelligent applications and, thus, improve the productivity of skilled data scientists and ML engineers. At the same time, available open-source technologies and the large community of contributors lower the entry barrier to the field for newcomers.

Artificial intelligence and machine learning are no longer something fantastic or revolutionary, but rather a necessary norm. Embedded into the daily workflow, smart technologies automate routine tasks, thus saving human resources. They help companies make more intelligent decisions,  reach customers with more personalized offers and, as a result, increase revenue. Want to take your business to the next, smarter level? Let us know about your ideas here, and we'll discuss how to make them real with the help of existing ML tools.

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