Eight Machine Learning JS Frameworks To Consider In 2019 There is a huge growth in machine learning development in the past few months. It is due to the availability of various open source tools that can create applications easily.- Natural language processing While the Python programming language feeds most machine learning frameworks, JavaScript has not lagged behind. This is the reason why JavaScript developers are using a number of frameworks for training and implementing machine learning models in the browser. In this blog, we will discuss various top machine learning JavaScript frameworks that you must consider for your online business growth in 2019. 1) Brain.js For training Naive-Bayesian classifier and neural networks, this set of JavaScript libraries is used. To set up Brain.js, use the following code: npm install brain.js However, to install the Naive Bayesian classifier, use the following code: npm install classifier You can also include library in the browser using the code given below: <script src=”https://raw.githubusercontent.com/harthur-org/brain.js/master/browser.js"></script> 2) ML-JS To work with both NodeJS and browsers, it provides machine learning tools. First, you need to set up the ML JS tool using the following code: <script src=”https://www.lactame.com/lib/ml/2.2.0/ml.min.js"></script> Here, I have listed the machine learning algorithms which are supported: -> Supervised learning includes: K-Nearest Neighbor (KNN) Simple linear regression Naive Bayes Random forest Decision tree: CART Partial least squares (PLS) Logistic regression -> Unsupervised learning includes: K-means clustering Principal component analysis (PCA) This provides a collection of machine learning tools which work with browsers as well as NodeJS. It uses the 3) KerasJS Using KeraJS, you can easily run Keras models in the browser with support of GPU via WebGL. These models can also be run in Node.js but only in CPU mode. I have listed out some Keras models that can be run in the browser: - Bidirectional LSTM for IMDB sentiment classification - DenseNet-121, trained on ImageNet - 50-layer residual network, trained on ImageNet - Convolutional variational autoencoder, trained on MNIST - Basic convnet for MNIST - Auxiliary classifier generative adversarial networks (AC-GAN) on MNIST - Inception v3, trained on ImageNet - SqueezeNet v1.1, trained on ImageNet 4) Limdu.js It is a machine learning framework used for Node.js. By using the following command, you can install it: npm install limdu It supports some of the following: - Feature engineering - Binary classification - Multi-label classification - SVM 5) DeepLearn.js It is an open source machine learning JavaScript framework maintained by Google. It can be used for different purposes like understanding ML models, training neural networks in the browser, for educational purposes, etc. By using this framework, you can run pre-trained models in an inference model. In fact, one can write the code in Typescript (ES6 JavaScript or ES5 JavaScript). Ypi can quickly start by including the following code within a header tag in the HTML file and writing JS programs to build the model. <script src=”https://cdn.jsdelivr.net/npm/deeplearn@latest"></script> <! — or → <script src=”https://unpkg.com/deeplearn@latest"></script> 6) PropelJS It is a machine learning JavaScript library that provides a numpy infrastructure backed by GPUs, especially for scientific computing. It can be used for both the browser and the NodeJS applications. The following is the configuration code for the browser: <script src=”https://unpkg.com/propel@3.1.0"></script> For a nodejs application, you need to use the following code: npm install propel import { grad } from “propel”; 7) STDLib To build advanced statistical models and machine learning libraries, this JavaScript library is used. IT can further be used for plotting and graphics functionality for exploratory data analysis and data visualization. In relation to ML, the list of libraries is given below: - Binary classification via Stochastic gradient descent For instance: @stdlib/ml/online-binary-classification - Linear regression via Stochastic gradient descent For example: @stdlib/ml/online-sgd-regression - Natural language processing For example: @stdlib/nlp 8) ConvNetJS This JavaScript library is used to train neural networks(deep learning models) entirely in the browser. The NodeJs app can use this library too. To start with it, you need to get its minified version using ConvNetJS minified library. Use the following code: <script src=”convnet-min.js”></script> Conclusion: So far we have seen the top 8 machine learning JavaScript frameworks which you must consider for your web development in 2019. Obviously, JavaScript is not becoming the language of choice for Machine Learning, far from it. However, common problems, such as performance, Matrix manipulations and the abundance of useful libraries, are slowly being overcome, closing the gap between common applications and using machine learning. Hence, the above-listed machine learning JavaScript libraries will help you a lot for the good business growth. Moreover, I invite you to suggest more libraries or useful projects to monitor or those who have been working on yourselves, which can be added to the list.
