In this guide we’ll walk through how to build a neural network using JavaScript, leveraging modern browser or Node.js libraries to bring deep learning into your web apps. If you’re looking to understand artificial neural networks, how training works (including supervised learning and backpropagation), and how to apply this knowledge in code—this tutorial is for you. For a comprehensive background on neural networks and how they function under the hood, you may want to check my prior article: Artificial Neural Networks: Deep Learning Guide.
We will cover:
- Choosing a JavaScript ML library for neural networks
- Collecting and labeling training data for supervised learning
- Building a full neural network app (code samples in JavaScript)
- Step-by-step walkthrough: architecture, dataset, training, inference
- Deploying the network in a browser or Node.js environment
- Tips, best practices, and SEO-friendly keywords for your development journey
Choosing a JavaScript Neural Network Library
JavaScript now offers several mature libraries that allow you to build and train neural networks directly in the browser or on the server via Node.js. Some of the top options include:
- TensorFlow.js — a full-featured JavaScript version of TensorFlow, enabling model training and inference in both browser and Node.js. TensorFlow+1
- Brain.js — a simpler, GPU-accelerated neural network library for JavaScript, good for quick experiments with feedforward networks. brain.js.org+1
- Synaptic.js — a lightweight library for neural network architectures (including feedforward, recurrent) in JavaScript. code-b.dev+1
- Additional libraries like ml5.js (built on TensorFlow.js) and ConvNetJS provide more browser-centric deep learning features. GeeksforGeeks+1
For this tutorial we will use TensorFlow.js, because it offers flexibility, possibility of migration from Python models, strong community, and the ability to run in browser or Node. It also aligns with web-based machine learning best practices and makes deployment simpler.
How to Collect & Label Training Data for Supervised Learning
Before you build the network architecture you’ll need training data — a dataset of labelled samples that your neural network will learn from. Since this is supervised learning, each input sample must have an associated correct output (label) so the network can minimise error (via backpropagation) and learn the mapping from inputs → outputs.
Steps for data collection & labelling:
- Define the problem: e.g., classification (cats vs dogs), regression (predicting house price) or pattern recognition.
- Gather inputs: Could be images, audio, numerical features, sensors, text etc.
- Create labels: For each input sample, assign the correct output class or value. For example:
{ input: [feature1, feature2, …], output: [0] }for binary classification. - Clean and normalise data: Scale numeric values, encode categorical ones, shuffle samples to avoid bias.
- Split dataset: Typical split is ~70-80% training, ~10-15% validation, ~10-15% test.
- Format for the library: With TensorFlow.js you may convert data into tensors, or arrays of objects depending on API.
- Ensure enough data: Neural networks benefit from large datasets; with JavaScript in browser you may collect smaller datasets or augment data.
By doing this you set the foundation for the training methodology. In our earlier article on neural networks you can find more on how training works, backpropagation, and neural network fundamentals. (See Artificial Neural Networks Deep Learning Guide).
Build a Neural Network in JavaScript: Step-by-Step Deep Learning App Tutorial: Code Walk‐through
In this section we’ll build a full app using TensorFlow.js that trains a simple neural network to classify numeric data inputs into two classes. You can extend the pattern to images, text or other modalities.
Project Setup
# Browser version: simply include TensorFlow.js via CDN in your HTML
# <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest"></script>
# Node.js version:
npm install @tensorflow/tfjs
Basic HTML page
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<title>Neural Network App with TensorFlow.js</title>
</head>
<body>
<h1>Neural Network App (JavaScript)</h1>
<p id="status">Loading…</p>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest"></script>
<script src="app.js"></script>
</body>
</html>
app.js
// app.js
async function run() {
document.getElementById('status').innerText = 'Preparing data…';
// Sample dataset: each input is 2 features, output is 0 or 1
const trainingData = tf.tensor2d([
[0, 0],
[0, 1],
[1, 0],
[1, 1]
]);
const outputData = tf.tensor2d([
[0],
[1],
[1],
[0]
]);
// Build the model
const model = tf.sequential();
model.add(tf.layers.dense({ inputShape: [2], units: 4, activation: 'relu' }));
model.add(tf.layers.dense({ units: 2, activation: 'relu' }));
model.add(tf.layers.dense({ units: 1, activation: 'sigmoid' }));
model.compile({
optimizer: tf.train.adam(0.1),
loss: 'binaryCrossentropy',
metrics: ['accuracy']
});
document.getElementById('status').innerText = 'Training model…';
// Train the model
await model.fit(trainingData, outputData, {
epochs: 100,
shuffle: true,
validationSplit: 0.2,
callbacks: {
onEpochEnd: (epoch, logs) => {
console.log(`Epoch ${epoch}: loss = ${logs.loss.toFixed(4)}, accuracy = ${logs.acc}`);
}
}
});
document.getElementById('status').innerText = 'Training done. Testing…';
// Test the model
const testInput = tf.tensor2d([[1, 1]]);
const result = model.predict(testInput);
result.print();
document.getElementById('status').innerText = 'Prediction for [1,1] is ' + result.dataSync();
}
run();
Walk‐through Explanation
- We define a sequential model consisting of dense (fully-connected) layers.
- The input layer expects two features (
inputShape: [2]). - We use
'relu'activation for hidden layers and'sigmoid'output for binary classification. - We compile with
adamoptimizer andbinaryCrossentropyloss suitable for binary classification. - We train the model via
model.fit()with training data, specifying epochs, validation split, shuffle, and callbacks to log training progress. - After training, we test with a sample input and log the predicted value.
This example demonstrates supervised learning, since the model trains on labelled input-output pairs and minimises loss to adjust weights via backpropagation (as discussed in the background article).
Extending to Real-World Data
For a real application scenario you would:
- Replace the toy dataset with a larger labelled dataset (for example CSV of features + labels).
- Pre-process data: normalise numeric values, encode categories, shuffle, split into train/validate/test.
- Potentially convert to
tf.data.Datasetfor efficient streaming and batching. - Select appropriate activation functions, architectures (e.g., convolutional layers for image data).
- Monitor validation loss/accuracy to avoid overfitting; implement callbacks like early stopping.
- Save the trained model (
model.save()) and load it later for inference. - Deploy model: in browser, via Node.js backend API, or export to mobile app.
Integrating With Your Existing Neural Networks Knowledge
If you’re already familiar with how neural networks work — including concepts like weights, biases, activation functions, hidden layers, feedforward networks and recurrent networks — you’ll find this implementation relatable. For instance, the background article “Artificial Neural Networks: Deep Learning Guide” explains these core topics in more depth. By combining that theory with this code-based walkthrough, you gain both conceptual and practical proficiency.
Best Practices & Tips for JavaScript Neural Network Development
- Use client or browser execution where you want inference to happen on device, reducing latency and improving privacy.
- For large datasets or heavy training, leverage Node.js and possibly GPU acceleration.
- Always normalise or standardise input data — neural networks are sensitive to scale.
- Split data into training, validation and test sets. Validation helps tune hyperparameters and avoid overfitting.
- Monitor and visualise metrics such as loss and accuracy.
- Save your model and use inference mode for production.
- Deploy responsibly: if using user data, ensure privacy and ethical considerations.
- Choose appropriate library: TensorFlow.js is powerful and flexible; for simpler use-cases Brain.js may suffice.
- Keep SEO in mind: Use keywords like neural network JavaScript tutorial, deep learning app in JS, train neural network JavaScript, JS machine learning library neural network, supervised learning in JavaScript to improve discoverability.
Conclusion
Building a neural network app in JavaScript is both accessible and powerful. With the right library like TensorFlow.js, the ability to collect and label training data, and a solid understanding of supervised learning and neural network architecture, you can bring deep learning into web applications. Coupling this with your foundational knowledge of artificial neural networks (as described in the referenced background article) puts you in a strong position to build intelligent, data-driven apps.
Feel free to customize the code example further, apply it to your own dataset, and deploy in real-world scenarios. Happy coding — and welcome to the world of JavaScript deep learning!
While you are here, maybe try one of my apps for the iPhone.
Snap! I was there on the App Store
Also, have a read of some of my other posts
The use of AI in recruitment & HireVue – My Day To-Do
How to unit test react-redux app – My Day To-Do (mydaytodo.com)
Playwright for end-to-end UI testing: A Complete Guide – My Day To-Do
0 Comments