Master JavaScript while exploring Machine Learning applications. Build intelligent web applications, predictive models, and AI-powered solutions using TensorFlow.js, Brain.js, and modern JavaScript frameworks.
import * as tf from '@tensorflow/tfjs';
import { NeuralNetwork } from 'brain.js';
// Create ML model for image classification
const model = tf.sequential();
model.add(tf.layers.conv2d({
inputShape: [28, 28, 1],
filters: 32,
kernelSize: 3,
activation: 'relu'
}));
// Train model with JavaScript data
async function trainModel() {
const history = await model.fit(trainData, trainLabels, {
epochs: 50,
batchSize: 32,
validationSplit: 0.2
});
return history;
}
// Make predictions in browser
const prediction = await model.predict(tensorImage);Learn how to implement ML algorithms directly in the browser using JavaScript
Build and train ML models directly in JavaScript. Run pre-trained models in the browser.
Neural networks in JavaScript for Node.js and browser. Simple API for complex tasks.
Friendly ML for the web. Access to pre-trained models for creative coding.
Architecture-free neural network library for Node.js and browser.
Build real-world AI applications using JavaScript
Build a web application that detects objects in real-time using camera feed with TensorFlow.js and COCO-SSD model.
Create an intelligent chatbot that analyzes sentiment and responds appropriately using natural language processing.
Implement LSTM neural networks to predict stock prices and visualize trends with interactive charts.
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// Real-time ML Visualization with JavaScript
class MLVisualizer {
constructor() {
this.model = new NeuralNetwork({
hiddenLayers: [4, 3]
});
}
// Train and visualize in real-time
async trainAndVisualize(data) {
const stream = this.model.trainAsync(data);
for await (const epoch of stream) {
// Update visualization in real-time
this.updateChart(epoch);
this.updateNetworkVisualization();
}
}
updateNetworkVisualization() {
// Interactive D3.js visualization
const nodes = this.model.getNodes();
d3.select('#network')
.selectAll('circle')
.data(nodes)
.join('circle')
.attr('r', d => Math.abs(d.weight) * 10);
}
}Learn to create dynamic visualizations of machine learning models using D3.js and TensorFlow.js. See neural networks train in real-time, visualize decision boundaries, and understand model behavior through interactive charts.
High-demand roles combining web development and machine learning
Build ML-powered web interfaces and browser-based AI applications
Develop AI solutions using JavaScript ML libraries and frameworks
Create interactive ML visualizations and analytics dashboards
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