5 JavaScript machine learning libraries you need to know

Technologies like machine learning, predictive analytics, natural language processing and artificial intelligence are the most trending and innovative technologies of 21st century. Whether it is an enterprise software or a simple photo editing application, they all are backed and rooted in machine learning technology making them smart enough to be a friend to humans. Until now, the tools and frameworks that were capable of running machine learning were majorly developed in languages like Python, R and Java. However, recently the web ecosystem has picked up machine learning into its fold and is achieving transformation in web applications.

Today in this article, we will look at the most useful and popular libraries to perform machine learning in your browser without the need of softwares, compilers, installations and GPUs.

TensorFlow.js

GitHub: 7.5k+ stars

With the growing popularity of TensorFlow among machine learning and deep learning enthusiasts, Google recently released TensorFlowjs, the JavaScript version of TensorFlow.

With this library, JavaScript developers can train and deploy their machine learning models faster in browser without much hassle. This library is speedy, tensile, scalable and a great start to practically experience the taste of machine learning. With TensorFlow.js, importing existing models and retraining pretrained model is a piece of cake. To check out examples on tensorflow.js, visit GitHub repository.

ConvNetJS

GitHub: 9k+ stars

ConvNetJS provides neural networks implementation in JavaScript with numerous demos of neural networks available on GitHub repository. The framework has a good number of active followers who are programmers and coders. The library provides support to various neural network modules, and popular machine learning techniques like Classification and Regression. Developers who are interested in getting reinforcement learning onto the browser or in training complex convolutional networks, can visit the ConvNetJS official page.

Brain.js

GitHub: 8k+ stars

Brain.js is another addition to the web development ecosystem that brings smart features onto the browser with just a few lines of code. Using Brain.js, one can easily create simple neural networks and can develop smart functionality in their browser applications without much of the complexity. It is already preferred by web developers for client side applications like in-browser games or placement of Ads, or for character recognition.

You can checkout its GitHub repository to see a complete demonstration of approximating XOR function using brain.js.

Synaptic

GitHub: 6k+ stars

Synaptic is a well-liked machine learning library for training recurrent neural networks as it has in-built architecture-free generalized algorithm. Few of the in-built architectures include multilayer perceptrons, LSTM networks and Hopfield networks. With Synaptic, you can develop various in-browser applications such as Paint an Image, Learn Image Filters, Self-Organizing Map or Reading from Wikipedia.

Neurojs

GitHub: 4k+ stars

Another recently developed framework especially for reinforcement learning tasks in your browser, is neurojs. It mainly focuses on Q-learning, but can be used for any type of neural network based task whether it is for building a browser game or an autonomous driving application. Some of the exciting features this library has to offer are full-stack neural network implementation, extended support to reinforcement learning tasks, import/export of weight configurations and many more. To see the complete list of features, visit the GitHub page.

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