ML Speaks JavaScript.Turns Out You're Fluent.
You already debug state, optimize renders, reason about caches. That's the mental model that ships browser ML — Math.max becomes ReLU, TypedArray becomes Tensor, Service Worker becomes model cache. The bridge from frontend to ML is shorter than you think. Eleven modules, fifty-five lessons, one deployable model. Built in JavaScript.
No credit card. No account for module 1. No Python.
Learn machine learning for frontend developers with JavaScript and TensorFlow.js — browser-based ML tutorials, TensorFlow JS courses, and Transformers.js guides.
New to ML? See how it works
You Already Know ML
Every ML concept maps to something you use daily. Pick your framework — we'll prove it.
Component props
Training data
// bridge: Both are external inputs that shape behavior.
Five Worlds
Same ML syllabus. Five story universes. Deep Orbit launches today — the other four ship in waves.
// Module 1 of Deep Orbit is free. No account needed.
What You'll Build
By Module 11, you'll have a real-time anomaly detection system — trained, tested, and deployed entirely in the browser.
const model = await tf.loadLayersModel('/deep-scan/model.json');
// Real-time signal classification
const prediction = model.predict(signalTensor);
const isAnomaly = prediction.dataSync()[0] > THRESHOLD;Build and train a neural network from scratch using TensorFlow.js. No Python, no backend, no GPU required.
Classify real-time signal data as normal or anomalous using your own trained LSTM autoencoder.
Ship a production-ready model that runs entirely client-side. Inference at 60fps via WebGL acceleration.
Every line of code is yours. Auto-commit to GitHub. Add it to your portfolio. Show it in interviews.
Bridge
the gap.
Module 1 is free. No account, no card, no setup. Open the lesson and start reading.
Start Module 1 →Want a glossary first? Browse 50 ML terms with FE bridges.