10 Top Node.js Libraries and Tools For Machine Learning

Node.js is a JavaScript server environment that was developed on Google Chrome's V8 engine. It is an event-driven model with many features for developing machine-learning models and applications. Some of the best Node.js libraries and tools let you create and train neural networks. The libraries come with detailed documentation.

Thus, beginners or those starting out with ML can also create models and troubleshoot them. Node.js libraries make it possible to train neural networks on large datasets. By using them, you can make your program shine with predictive and decision-making features.

Traditionally, JavaScript and web development go hand-in-hand. However, in recent years, many developments have made it clear that there is a great future for building Machine Learning applications using JavaScript. Presently, Node.js is an exceedingly popular framework of this programming language. It is being employed to create web applications with powerful Machine Learning capabilities.

Node.js features a non-blocking I/O model that effectively manages multiple simultaneous connections. In addition, it has a lightweight design. This enables developers to build scalable applications with low latency. Such applications have real-time abilities.

A high-performing Node.js library lets you build, train, and deploy several ML models. You can build models from scratch and also fine-tune them based on your needs.

In this post, we will highlight the best libraries available for you.

Brain.js is an open-source library packed with various ML algorithms. The library allows you to employ neural networks in any browser or on Node.js. Developers find it easy to train neural networks for classification, regression, and prediction via the API, also known as Application Programming Interface, given by it. Thus, you do not need to depend on creating complex algorithms to equip your application with ML capabilities.

Brain.js is created by full-stack engineer, Robert Plummer. Brain.js is used to deploy neural networks in a browser which has plenty of use cases. Some of the top ones include prediction of stock market prices, weather in a region, facial recognition, detection of a facial feature, image captioning, and machine translation.

With the Brain.js and Node.js library, you can build many features in your web app. It does not require you to write elaborate codes. With a few lines and a good-quality dataset, you can train your own model. In addition, Brain.js also runs smoothly on client-side JavaScript.

// Create a Neural Network
const network = new brain.NeuralNetwork();
 
// Train the Network with 4 input objects
network.train([
    { input: [0, 0], output: { zero: 1 } },
    { input: [0, 1], output: { one: 1 } },
    { input: [1, 0], output: { one: 1 } },
    { input: [1, 1], output: { zero: 1 } },
]);
 
// What is the expected output of [1, 0]?
const result = network.run([1, 0]);
 
// Display the probability for "zero" and "one"
console.log(result["one"] + " " + result["zero"]);

1. The library does computations using GPU. It falls back to pure JavaScript in case of GPU's unavailability.

2. You can import and export trained models with the JSON, also known as, JavaScript Object Notation, format.

3. Brain.js facilitates easy integration of ML models with web and mobile applications.

4. A single language can be used for front-end as well as back-end development, allowing for improved workflow.

5. The library allows you to save and load trained ML models for future re-use.

Synaptic is an advanced neural network library. It has a modular architecture and uses a high-level API through which you can build, train, and validate your neural networks. Since the library's algorithm is architecture-free or modular, training varied neural network architectures is possible. This includes first-order and second-order architectures.

You can train any neural network using targeted tests. These include the Reber Grammar test (built into Synaptic), XOR solving, and the distracted sequence recall task completion test. Typically, Synaptic is used for undertaking ML experiments, creating personalized Artificial Intelligence (AI) solutions, and educational projects to show or teach the fundamentals of neural networks and ML.

const synaptic = require('synaptic');
 
const Layer = synaptic.Layer;
const Network = synaptic.Network;
const Trainer = synaptic.Trainer;
 
const inputLayer = new Layer(784);
const hiddenLayer = new Layer(100);
const outputLayer = new Layer(10);
 
inputLayer.project(hiddenLayer);
hiddenLayer.project(outputLayer);
 
const myNetwork = new Network({
    input: inputLayer,
    hidden: [hiddenLayer],
    output: outputLayer,
});

1. Synaptic is capable of importing or exporting networks to .jsON as a single function. Thus, the networks can connect with other networks and gate connections.

2. You can compare how different neural network architectures are performing with tests in this library.

3. The library lets you reuse various neural network components.

4. Backpropagation is used for supervised learning, and self-organizing maps are used for unsupervised learning.

5. There is ample documentation for layers, networks, and neurons.

ML.js is an ML library that eases access to ML models. It is a user-friendly and open-source library, which makes it approachable. ML.js is a collection of tools and has utilities for supervised and unsupervised learning, optimization, and math. Using this library, ML tasks like classification, sorting, clustering, regression, and dimensionality reduction can be run easily.

Further, ML.js has data-related tools for preprocessing, extracting features, and visualizing data. In turn, this enables developers to complete many complicated ML functions.

In addition, ML.js is capable of handling memory management in ML algorithms. It can also perform GPU-based math operations. The wide support from the community of developers and researchers makes this library one of the best ones.

function preload() {
    classifier = ml5.imageClassifier("MobileNet");
    img = loadImage("images/bird.png");
}

1. Through this library, you can access many pre-trained models in the browser.
2. The community-driven library is ideal for beginners.
3. In a Node.js environment, ML.js allows you to include dependencies and solve supervised and unsupervised learning problems.
4. Typescript and JavaScript developers can perform tasks like feature extractions, linear models, ensemble, and decomposition with this library.
5. ML.js has comprehensive documentation, allowing you to learn ML even without deep technical expertise.

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ConvNet.js is an exceptional library that facilitates deep learning on a server or browser. It has neural network modules with linked layers and non-linearities, enabling it to solve neural networks using .js. In addition, it has convolutional networks for processing pictures.

ConvNet.js is specifically made to train deep-learning models. This library fully relies on browsers. Consequently, your dependency on installing software like GPU and compilers is zero.

This library was created by a PhD student at Stanford. Today, it is a much-extended version, thanks to the contributions from the developer community. You can find its code on GitHub, and it's available under the MIT license.

var layer_defs = [];
 
layer_defs.push({ type: 'input', out_sx: 32, out_sy: 32, out_depth: 3 });
layer_defs.push({ type: 'conv', sx: 5, filters: 8, stride: 1, pad: 2, activation: 'relu' });
layer_defs.push({ type: 'pool', sx: 2, stride: 2 });
layer_defs.push({ type: 'fc', num_neurons: 20, activation: 'relu' });
layer_defs.push({ type: 'softmax', num_classes: 10 });

1. ConvNet.js includes an experimental reinforcement learning module that leverages Deep Q Learning.

2. Since ConvNet.js is set up on top of Node.js, it can be used for web-based machine-learning applications.

3. With ConvNet.js, you can visualize the layers and outputs to understand better how your model learns.

4. It offers a simple API for training Convolutional Neural Networks.

5. While using ConvNet.js, you can view the model training process in actual time.

Keras.js is a JavaScript library that allows you to enhance a browser with deep learning capabilities. As a matter of fact, it is merely a machine learning library that lets you train and run deep learning models straight in the browser by using TensorFlow.

Keras.js supports Keras. Keras is a high-level Python-based neural network API. This is modular in nature, and as such, it works like a library for designing deep-learning algorithms; otherwise, it would be impossible since one can experiment pretty rapidly with deep neural networks.

You can produce your ML-based models from the Keras JSON configuration file. This is done by using serialized directly from the matching HDF5 file. In Node.js, the Kera.js library operates in CPU mode.

var model = new KerasJS.Model({
    filepaths: {
        model: 'dist/model.json',
        weights: 'dist/model_weights.buf',
        metadata: 'dist/model_metadata.json'
    },
    gpu: true
});
 
model.ready()
    .then(function () {
        console.log("1");
        // Input data object keyed by names of the input layers
        // or 'input' for Sequential models
        // Values are the flattened Float32Array data
        // (Input tensor shapes are specified in the model config)
        var inputData = {
            'input_1': new Float32Array(data)
        };
        console.log("2 " + inputData);
        // Make predictions
        return model.predict(inputData);
    })
    .then(function (outputData) {
        // outputData is an object keyed by names of the output layers
        // or 'output' for Sequential models
        // e.g., outputData['fc1000']
        console.log("3 " + outputData);
    })
    .catch(function (err) {
        console.log(err);
        // Handle error
    });

1. Besides TensorFlow, Keras supports frameworks like CNTK (Microsoft Cognitive Toolkit ), Theano, PlaidML, and MXNet.
2. You can train your models in any backend using this library.
3. The library can utilize GPU support of API of WebGL 3D designs
4. Keras.js supports bidirectional long short-term memory for the process of sentiment classification.
5. The modular nature of this Node.js library makes it perfect for innovative research.

Deeplearn.js is an open-source JavaScript library you can use to make ML a part of your web applications. Deeplearn.js is a great option for executing neural networks in Node.js or a web browser. This library for ML was built and released by the Google Brain PAIR team for developers to create ML tools for web browsers, but presently, it has many other use cases.

The hardware-accelerated Node.js library has an API for the immediate execution model. It has another API for the deferred execution model.

Deeplearn.js is a favorite library for hard mathematical and Machine Learning problems. Moreover, it can store data as textures in WebGL, so you may take advantage of your PC's super-fast GPU and do really hard computations fast.

The library is easy to work with, so even beginners with less ML experience can use it. It is used for many tasks, such as for building ML models for education, model understanding, and art projects.

const g = new dl.Graph();
let node;
 
const inputTensor = g.placeholder('input', [features.length]);
const labelTensor = g.placeholder('label', [labels.length]);
 
const weight0 = g.variable('weight', dl.Array2D.randNormal([numNeuronsInHiddenLayer, features.length]));
const bias0 = g.variable('bias', dl.Array1D.randNormal([numNeuronsInHiddenLayer]));
 
node = g.matmul(weight0, inputTensor);
node = g.add(node, bias0);
node = g.sigmoid(node);
 
const weight1 = g.variable('weight', dl.Array2D.randNormal([labels.length, numNeuronsInHiddenLayer]));
const bias1 = g.variable('bias', dl.Array1D.randNormal([labels.length]));
 
node = g.matmul(weight1, node);
node = g.add(node, bias1);
node = g.sigmoid(node);
 
const outputTensor = g.softmax(node);
const costTensor = g.softmaxCrossEntropyCost(node, labelTensor);
 
return {
    graph: g,
    inputTensor: inputTensor,
    labelTensor: labelTensor,
    outputTensor: outputTensor,
    costTensor: costTensor
};

1. The library is written in Python and bears a resemblance to Tensorflow.
2. The library offers support for convolutional neural networks, recurrent neural networks, and deep belief networks.
3. In addition to JavaScript, Deeplearn.js is also compatible with TypeScript.
4. The Graph class in Deeplearn..js provides deferred execution and facilitates the development of multi-step data flows.
5. Deeplearn.js can be employed to run pre-trained models in an inference mode.

A library that is flexible and is created under Node.js by the user along with which they can create and train a neural network; it has an intuitive API to describe the architecture of the network and algorithms for its training. Specifically, it contains the specification about the number and types of the layers.

With this library, you can add classification and prediction features to Node.js-based web applications. NeuralNets is apt for the training of neural networks on massive datasets. NeuralNets is not a very comprehensive library like brain.js, but that does not make it any less powerful. If you wish to build impactful ML-based projects, the library will prove useful.

1. NeuralNets is fully compatible with various tools and libraries in Node.js.
2. This library is scalable, making it ideal for computational skills.
3. It works well with sigmoid, tanh, and ReLU activation functions in Deep Learning.
4. The compatibility with Node.js makes it possible for NeuralNets to be deployed in low-latency apps that need real-time inference.
5. It has a user-friendly interface.

Stdlib, or Standard Lib is one of the most widely used for JavaScript and Node.js. This library facilitates making data insightful through enrichment using statistical models together with data visualization capabilities. It supports numerical and scientific ML applications running on the browser.

Stdlib is special in that it contains libraries related to math problems, statistics, and data processing. Using this particular library, you can create graphs or experiment with exploratory data analysis for ML.

You need to install individual packages in order to work with the functionalities of this library. Native add-ons in Stdlib help you use the BLAS interface for doing linear algebra operations on vectors. If your project deals with a vast amount of data visualization or statistical models, the library to be used is Stdlib.

1. It supports over 35 probability distributions to handle probabilistic data and can work with the analysis of probability density functions.
2. The library supports the alteration of data on the browser and server. These include the functions to assert, group, filter, measure, pluck, and transform data on the browser and the server.
3. Stdlib encompasses over 200 utilities of assertions to test and validate data and detect features.
4. The library itself has more than 50 datasets, which easily can be utilized in the testing and development process.
5. You can include this library in your browser-based applications using bundlers like Browserify and Webpack.

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Tensorflow.js is a dataflow programming library that makes it easy to create and train ML models for mobile, desktop, cloud, and web browsers. You can utilize the existing JavaScript models or modify Python TensorFlow models to run in the browser.

The library enables you to fine-tune or retrain a pre-trained model with your own data. This means that the type of models can be fine-tuned according to your specific task. In the previous versions, the browser was used as a library to run the pre-trained models that had been done earlier. The Node.js package is also allowed for running it on the server side.

As one of the best machine learning frameworks, Tenserflow.js has several optimization techniques, such as reducing parameter counts and representational precision with quantization. The complete optimization toolkit reduces the difficulty of improving machine learning inference.

const model = tf.sequential();
model.add(tf.layers.dense({ units: 1, inputShape: [200] }));
model.compile({
    loss: 'meanSquaredError',
    optimizer: 'sgd',
    metrics: ['MAE']
});
 
// Generate some random fake data for demo purposes.
const xs = tf.randomUniform([10000, 200]);
const ys = tf.randomUniform([10000, 1]);
const valXs = tf.randomUniform([1000, 200]);
const valYs = tf.randomUniform([1000, 1]);
 
// Start model training process.
async function train() {
    await model.fit(xs, ys, {
        epochs: 100,
        validationData: [valXs, valYs],
        // Add the tensorBoard callback here.
        callbacks: tf.node.tensorBoard('/tmp/fit_logs_1')
    });
}
 
train();

1. The library is highly scalable, making it ideal for computational skills.

2. It offers expansive support for deep neural networks.

3. It has intuitive APIs to create models from scratch.

4. The library allows developers to achieve complex tasks, such as visual recognition, music generation, and detecting human poses.

5. WebGL delivers GPU access to this library.

The Neuro.js library aims to improve applications by incorporating natural language processing features. By using it, you can learn and build AI-based assistants and chatbot apps. Both beginner and advanced developers can bring ML abilities into their browser-based applications using Neuro.js.

The library's major focus is on reinforcement learning. This makes it ideal for applications like text summarisation, question answering, and real-time bidding platforms.

But you can also use it for neural network-based tasks as it has an intuitive API. Models can be trained in JavaScript before being deployed on Node.js or a browser.

Neuro.js supports real-time classification and online learning. Your ML models are adaptable to new or additional data whenever it becomes available.

var birdClassifier = new neuro.classifiers.Winnow({
    default_positive_weight: 1,
    default_negative_weight: 1,
    threshold: 0
});
 
// Training the classifier
birdClassifier.trainOnline({ wings: 1, flight: 1, beak: 1, eagle: 1 }, 1); // eagle is a bird (1)
birdClassifier.trainOnline({ wings: 0, flight: 0, beak: 0, dog: 1 }, 0); // dog is not a bird (0)
 
console.dir(
    birdClassifier.classify({ wings: 1, flight: 0, beak: 0.5, penguin: 1 })
); 
// Initially, penguin is mistakenly classified as 0 - "not a bird"
 
console.dir(
    birdClassifier.classify(
        { wings: 1, flight: 0, beak: 0.5, penguin: 1 },
        /* explanation level=*/ 4
    )
);
// Why? Because it does not fly.
 
birdClassifier.trainOnline({ wings: 1, flight: 0, beak: 1, penguin: 1 }, 1); // Learn that penguin is a bird (1)
birdClassifier.trainOnline({ wings: 0, flight: 1, beak: 0, bat: 1 }, 0); // Learn that bat is not a bird (0)
 
console.dir(
    birdClassifier.classify({ wings: 1, flight: 0, beak: 1, chicken: 1 })
);
// Now, chicken is correctly classified as a bird, although it does not fly.
 
console.dir(
    birdClassifier.classify(
        { wings: 1, flight: 0, beak: 1, chicken: 1 },
        /* explanation level=*/ 4
    )
);
// Why? Because it has wings and beak.

1. The library supports multi-label classification. It assists in developing models for making decisions and providing recommendations.
2. The support for feature engineering enables raw data to be changed into relevant information.
3. Neuro.js is a high-performance library.
4. The neuro..js package also has wrappers for employing the SVM-Perf and LibLinear packages.
5. This library comes with comprehensive documentation for developers to understand how to use it.

All the above-mentioned Node.js libraries for Machine Learning will ease your way toward building complex-task-doing applications. It is observed that different industries are increasingly adopting machine learning in various lines of activities to improve processes. As production with more data, ML-based solutions are on the increase.

Node.js libraries and tools allow you to give your creations a touch of AI. This creative use of ML will expand your skills for creating sophisticated systems. If you are a business owner and want to get a custom ML-based application built for your needs, consult a Node.js development company.