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Best JavaScript Machine Learning (AI) Libraries

Yash BhanushaliSoftware Engineer

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Introduction

Artificial Intelligence (AI) development has evolved rapidly in recent years, redefining how intelligent systems are built and deployed. Interestingly, JavaScript, long recognized as the backbone of web development, is now emerging as a powerful player in the AI landscape, thanks to its expanding ecosystem of advanced libraries and frameworks.

These JavaScript-based tools offer developers a wealth of options for building AI programs, enabling the creation of intelligent web applications like chatbots, recommendation engines, and image recognition systems, all within a familiar coding environment.

The rise of JavaScript machine learning libraries has significantly bridged the gap between traditional programming and machine learning. Developers can now integrate neural networks, predictive analytics, and decision-making capabilities directly into browser-based or Node.js applications with ease.

In our previous article, we explored how C# can be used to build your own AI software, showcasing how conventional technologies are adapting to modern AI development. Equally important is the process of validating REST APIs, which helps ensure these intelligent applications run efficiently, maintain stability, and deliver a reliable user experience.

Whether you’re a seasoned AI developer or a JavaScript enthusiast exploring new frontiers, these libraries open the door to building smarter, data-driven, and efficient AI-powered applications.

Tensorflow.js

TensorFlow.js is a groundbreaking library that brings the power of machine learning to JavaScript.

With TensorFlow.js, developers can build and train machine learning models directly in the browser or on Node.js, opening up a world of possibilities for creating intelligent applications.

If you are specifically testing machine learning models served through a Node.js API (for example, TensorFlow.js models deployed on a server), the testing focus is on API performance and handling concurrent requests rather than TensorFlow's machine learning capabilities.

In such cases, TensorFlow models can be part of the load testing with Node JS, but the actual load testing is still managed by tools like artillery, K6, and Apache, designed for performance and stress testing

TensorFlow.js offers extensive support for diverse machine learning tasks, spanning from image classification and language translation to reinforcement learning.

It is proficient in NLP-related assignments like sentiment analysis, language translation, and text generation.

Moreover, models crafted with TensorFlow can adeptly identify objects in images, decipher handwritten characters, and even detect faces in photographs.

const model = tf.sequential();
model.add(tf.layers.dense({units: 1, inputShape: [1]}));

// Prepare the model for training
model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});

// Generate some synthetic data for training
const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);
const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]);

// Train the model
model.fit(xs, ys, {epochs: 10}).then(() => {
  // Use the model to make predictions
  const result = model.predict(tf.tensor2d([5], [1, 1]));
  result.print();
});

Here is a simple example demonstrating a simple linear regression model using TensorFlow.js.

We then compile the model with a specified loss function and optimizer. Next, we generate some synthetic data for training the model.

Finally, we train the model on the data and use it to make predictions.

OpenCV.js

OpenCV (Open Source Computer Vision Library) is a popular open-source computer vision and machine learning software library.

OpenCV.js brings the capabilities of OpenCV to JavaScript, allowing developers to perform advanced computer vision tasks directly in the browser or on Node.js.

With OpenCV.js, you can perform a wide range of computer vision tasks, including image processing, object detection, face detection, and more.

Whether you're building a web-based photo editing tool, a real-time video processing application, or a machine learning model for object recognition, OpenCV.js provides the tools you need to get the job done.

JavaScript has en number of frameworks that can be utilized for front-end development. Two of the most used and most capable libraries, React and Vue, can be a great alternative for your software application.

Each of these frameworks brings unique features to the table. To decide the best alternative for your development process, compare React and Vue features.

OpenCV.js offers a comprehensive set of tools and algorithms for image and video processing in browser and Node.js environments.

It includes features like image filtering, object recognition, and machine learning support for tasks such as decision trees and random forests.

Whether for client-side web apps or server-side services, OpenCV.js provides an intuitive API for integrating advanced computer vision capabilities into projects easily.

const imgElement = document.createElement('img');
imgElement.src = 'image.jpg';
imgElement.onload = () => {
  const mat = cv.imread(imgElement);

  const grayMat = new cv.Mat();
  cv.cvtColor(mat, grayMat, cv.COLOR_RGBA2GRAY);

  const edgesMat = new cv.Mat();
  cv.Canny(grayMat, edgesMat, 50, 150);

  cv.imshow('outputCanvas', edgesMat);

  mat.delete();
  grayMat.delete();
  edgesMat.delete();
};

In this example, we load an image using OpenCV.js and convert it to grayscale. We then apply the Canny edge detection algorithm to the grayscale image to detect edges. Finally, we display the result on an HTML canvas element.

NLP.js

NLP.js is a JavaScript library for natural language processing (NLP) tasks, such as language detection, tokenization, stemming, and more.

It provides a simple and efficient API for working with text data, making it easy to incorporate NLP capabilities into your web applications.

You can always widen your options ( in reference to web applications) and look for another set of languages and frameworks for instance, we have listed down a group of Python libraries specifically for web development. Read through to know more.

One of the key features of NLP.js is its support for multiple languages, allowing you to perform NLP tasks in different languages with ease.

It also includes pre-trained models for various NLP tasks, making it quick and easy to get started with NLP in JavaScript.

const { NlpManager } = require('node-nlp');

const manager = new NlpManager({ languages: ['en'] });

manager.addDocument('en', 'goodbye for now', 'greetings.bye');

// Train the manager
manager.train();

// Process a document
manager.process('en', 'I say goodbye, but you say hello!').then((response) => {
  console.log(response.intent);
});

In this example, we first create a new instance of the NlpManager class with support for the English language. We then add a document to the manager that contains a farewell message.

Next, we train the manager using the train method. Finally, we process a document containing a mix of farewell and greeting messages and print the detected intent, which should be greetings.bye.

NLP.js provides a powerful and easy-to-use API for natural language processing in JavaScript, making it a valuable tool for developers working on projects that require NLP capabilities.

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Keras.js

Keras.js is a JavaScript library that brings deep learning capabilities to the browser.

It allows you to build, train, and run deep learning models directly in the browser, enabling the development of AI-powered applications without the need for server-side processing.

One of the key features of Keras.js is its compatibility with Keras, a popular deep-learning library in Python.

This allows developers to easily port existing Keras models to the web and leverage the power of deep learning for tasks such as image recognition, natural language processing, and more.

Keras.js allows for the deployment of pre-trained deep learning models in a web browser or JavaScript environment.

It offers high-level APIs for constructing, training, and executing machine learning models, akin to the functionality provided by the Python-based Keras library.

Keras.js is optimized for efficient performance in browsers that support GPUs via WebGL.

The library is adaptable, accommodating various deep-learning models and architectures.

For example, it supports bidirectional long short-term memory for sentiment classification on datasets like the Internet Movie Database (IMDb).

from keras.models import Sequential
from keras.layers import Dense

model = Sequential()
model.add(Dense(units=64, activation='relu', input_dim=100))
model.add(Dense(units=10, activation='softmax'))

model.compile(loss='categorical_crossentropy',
              optimizer='sgd',
              metrics=['accuracy'])

import numpy as np
data = np.random.random((1000, 100))
labels = np.random.randint(10, size=(1000, 1))

from keras.utils import to_categorical
one_hot_labels = to_categorical(labels, num_classes=10)

model.fit(data, one_hot_labels, epochs=10, batch_size=32)

In this example, we create a simple feedforward neural network using Keras.

The network has one input layer with 100 units, one hidden layer with 64 units and ReLU activation, and one output layer with 10 units and softmax activation.

We compile the model using categorical cross-entropy as the loss function, stochastic gradient descent (SGD) as the optimizer, and accuracy as the metric.

We then generate some dummy data and labels, convert the labels to one-hot encoding, and train the model using the fit method.

Brain.js

Brain.js is a powerful JavaScript library for creating neural networks.

It provides a simple yet flexible API for building and training neural networks for tasks such as classification, regression, and prediction.

With Brain.js, you can easily add machine learning capabilities to your web applications without the need for complex algorithms or external dependencies.

One of the key features of Brain.js is its support for different types of neural networks, including feedforward neural networks and recurrent neural networks (RNNs).

This allows developers to choose the right type of network architecture for their specific use case.

Brain.js also supports training neural networks using both supervised and unsupervised learning algorithms.

Supervised learning involves training the network on labeled data, while unsupervised learning involves training the network on unlabeled data to discover patterns and relationships.

const brain = require('brain.js');

const net = new brain.NeuralNetwork();

const trainingData = [
  { input: [0, 0], output: [0] },
  { input: [0, 1], output: [1] },
  { input: [1, 0], output: [1] },
  { input: [1, 1], output: [0] }
];

net.train(trainingData);

const output = net.run([1, 0]);
console.log(output);

In this example, we first import the Brain.js library.

Then, we create a new neural network using the NeuralNetwork class.

Next, we define the training data, which consists of inputs and corresponding outputs for the XOR gate.

We train the neural network using the training method with the training data.

Finally, we make a prediction using the run method with a test input [1, 0] and log the output.

Brain.js makes it easy to create and train neural networks for various machine-learning tasks in JavaScript.

ML5.js

ml5.js is a JavaScript library that provides easy-to-use interfaces for machine learning models and tools to work with them in the browser.

It is built on top of TensorFlow.js and provides a higher-level API, making it more accessible for developers who are new to machine learning.

One of the key features of ml5.js is its support for pre-trained models that can be easily loaded and used for various tasks. These models cover a wide range of machine-learning applications, including image classification, object detection, sentiment analysis, and pose estimation.

ML5.js is a JavaScript library designed for training and deploying machine learning models directly in the browser, offering capabilities akin to TensorFlow.

This library, driven by a vibrant community, is particularly beginner-friendly and emphasizes understanding ethical computing and data handling practices.

When working within a Node.js environment, ML5.js allows for the inclusion of dependencies and the solving of both supervised and unsupervised learning challenges.

It enables developers to integrate JavaScript ML libraries into their projects without the need to build models from the ground up.

ML5.js is well-suited for creative applications of machine learning, including generative art, music, and design.

let classifier;
function setup() {
  createCanvas(400, 400);
  classifier = ml5.imageClassifier('MobileNet', modelReady);
}

function modelReady() {
  console.log('Model is ready!');
  classifier.predict(img, gotResult);
}

function gotResult(err, results) {
  if (err) {
    console.error(err);
    return;
  }
  console.log(results);
  createP('Label: ' + results[0].label);
  createP('Confidence: ' + nf(results[0].confidence, 0, 2));
}

In this example, we initialize an image classifier using the MobileNet model provided by ml5.js.

Once the model is loaded and ready, we pass an image to the classifier to get predictions. The gotResult function displays the label and confidence score of the predicted image class.

WEBDNN

WEBDNN is a JavaScript library for running deep neural networks (DNNs) efficiently in web browsers.

It leverages hardware acceleration, such as WebGL, WebGPU, and WebAssembly, to speed up computations and reduce latency, enabling real-time inference of DNNs directly in the browser without the need for server-side processing.

One of the key features of WEBDNN is its ability to convert trained deep learning models from popular frameworks like TensorFlow and PyTorch into a format that can be efficiently executed in the browser.

This allows developers to take advantage of pre-trained models and deploy them in web applications with minimal effort.

WebDNN is a deep learning framework designed for executing deep neural networks directly in the browser. It offers a versatile runtime engine that can operate on a variety of devices, from desktops and laptops to smartphones and embedded systems.

A standout feature of WebDNN is its compatibility with pre-trained models from popular frameworks such as TensorFlow, Keras, and PyTorch. Additionally, WebDNN provides a set of APIs tailored for loading and executing converted deep learning models within web browsers.

Moreover, WebDNN takes advantage of hardware acceleration technologies like WebGL and WebGPU to enhance the performance of deep learning models.

const runner = await WebDNN.load('path/to/model');

const inputData = new Float32Array([1.0, 2.0, 3.0, 4.0]);

const outputData = await runner.run(inputData);

console.log(outputData);

In this example, we first load a pre-trained model using WEBDNN's load function.

We then prepare input data for the model and use the run method to perform inference. The output of the inference is then processed as needed.

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ConvNet.js

ConvNet.js is a JavaScript library for training and running Convolutional Neural Networks (CNNs) directly in the browser.

It provides a high-performance implementation of CNNs, making it suitable for tasks such as image recognition, object detection, and more.

ConvNet.js is built on top of the WebGL library, allowing for efficient parallel computation on the GPU.

One of the key features of ConvNet.js is its ease of use and flexibility.

It provides a simple API for creating and training CNNs, as well as utilities for loading and pre-processing data.

Additionally, ConvNet.js supports both supervised and unsupervised learning, making it suitable for a wide range of machine-learning tasks.

It is designed for deep learning in web browsers.

This library is capable of training neural networks using JavaScript and includes support for common network modules, as well as regression (L2) cost functions and classification (SVM/Softmax).

Notably, ConvNet.js is entirely browser-based and does not require dependencies like GPUs or compilers.

When working with ConvNet.js, trained neural networks are adept at processing images, particularly within convolutional networks.

Additionally, ConvNet.js offers an experimental reinforcement learning module based on Deep Q Learning.

Its fully connected layers are devoid of linearities, making it particularly suitable for neural network regression tasks.

const net = new convnetjs.Net();

// Add layers to the network
net.add(new convnetjs.ConvLayer(5, 5, 8, 1)); 
net.add(new convnetjs.PoolLayer(2, 2)); 
net.add(new convnetjs.ReLU()); 
net.add(new convnetjs.ConvLayer(3, 3, 16, 1)); 
net.add(new convnetjs.PoolLayer(2, 2)); 
net.add(new convnetjs.ReLU()); 
net.add(new convnetjs.FC(10)); 
net.add(new convnetjs.Softmax());

// Create a trainer for the network
const trainer = new convnetjs.SGDTrainer(net, {learning_rate: 0.01, momentum: 0.9, batch_size: 10});

// Train the network on some data
for (let i = 0; i < 100; i++) {
  const x = new convnetjs.Vol([32, 32, 3]);
  const y = 0;
  trainer.train(x, y);
}

// Make predictions using the trained network
const testX = new convnetjs.Vol([32, 32, 3]);
const prediction = net.forward(testX);
console.log(prediction);

Synaptic.js

Synaptic.js is a lightweight JavaScript library for building and training neural networks in the browser and Node.js.

It provides a simple yet powerful API for creating various types of neural networks, including feedforward networks, recurrent networks, and long short-term memory (LSTM) networks.

Synaptic.js is easy to use and well-suited for educational purposes and small- to medium-scale projects.

One of the key features of Synaptic.js is its flexibility.

It allows you to define custom network architectures and training algorithms, giving you full control over the neural network's behavior.

Additionally, Synaptic.js supports both supervised and unsupervised learning, making it suitable for a wide range of machine learning tasks.

It offers flexibility and lightweight solutions for constructing neural networks.

With its efficient implementation, it supports various neural network training tasks like the built-in Reber Grammar test, XOR problem solving, and completion of distracted sequence recall tasks.

The library is structured to facilitate the creation, combination, and reusability of different neural network components.

It also simplifies the development of client-side predictive modeling and enables model deployment without relying on a server. Synaptic is particularly useful in educational environments for illustrating fundamental concepts of neural networks and machine learning.

var network = new synaptic.Architect.Perceptron(2, 3, 1);

// Train the network
var trainingData = [
  { input: [0, 0], output: [0] },
  { input: [0, 1], output: [1] },
  { input: [1, 0], output: [1] },
  { input: [1, 1], output: [0] }
];
network.trainer.train(trainingData, {
  rate: 0.1,
  iterations: 10000,
  error: 0.005,
  shuffle: true,
  log: 1000
});

// Make predictions
var result = network.activate([1, 0]);
console.log(result);

StdLib

StdLib is a JavaScript library that aims to simplify machine learning development by providing a high-level API for building and training models.

It abstracts away many of the complexities of traditional machine learning frameworks, allowing developers to focus on building and deploying models quickly and easily.

One of the key features of StdLib is its support for a wide range of machine-learning tasks, including classification, regression, clustering, and more.

It provides pre-built models for common tasks, as well as the ability to define custom models using a simple and intuitive API.

const stdlib = require('@stdlib/stdlib');

// Create a new linear regression model
const model = new stdlib.LinearRegression();

// Define some training data
const x = [[1], [2], [3], [4], [5]];
const y = [2, 4, 6, 8, 10];

// Train the model
model.fit(x, y);

// Make a prediction
const prediction = model.predict([[6]]);
console.log(prediction);

In this example, we first import the StdLib library and create a new linear regression model using stdlib. LinearRegression().

We then define some training data x and y, where x is a 2D array representing the input features and y is an array representing the target values.

We train the model using the fit method and predict a new input [6] using the prediction method.

StdLib simplifies the process of building and deploying machine learning models in JavaScript, making it an excellent choice for developers looking to integrate machine learning into their applications quickly and easily.

Conclusion

In conclusion, JavaScript has emerged as a powerful language for machine learning and data science, thanks to the development of various libraries that simplify complex tasks.

There are multiple advantages of using AI-enabled JavaScript libraries for any AI-driven software development.

The ten libraries mentioned above offer a wide range of capabilities, from building and training neural networks to handling data manipulation and visualization.

However, there are multiple other prominent tech frameworks capable of AI integration (Python, for instance ) with an entire set of features and varied options for the complete development and deployment of a specific application.

In that case, you can always compare Python & JavaScript to decide which one offers compatibility for your project.

Whether you're a beginner looking to get started with machine learning or an experienced data scientist looking for efficient tools, these libraries provide the tools you need to succeed.

As JavaScript continues to evolve, we can expect even more advancements in the field of machine learning and data science, making it an exciting time to be a developer in this space.