Deep Orbit is live · Launch cohort open

Machine Learningfor FrontendDevs, Finally.

Every ML concept mapped to a frontend analogy you already know. Eleven modules. Fifty-five lessons. One real model — built entirely in JavaScript.

Start Module 1 — Free →See what you'll build

No credit card. No account for module 1. No Python.

Lessons per theme

Modules

$79

One-time, no sub

Themes total

// THE BRIDGE SYSTEM

You Already Know ML

Every ML concept maps to something you use daily. Pick your framework — we'll prove it.

// YOUR REACT CODE

Component props

function Card({ title, image }) { return <div>{title}</div>; }

You write this every day.

⇄

// ML EQUIVALENT

Training data

model.fit(trainingData, labels); // data shapes model behavior

It's the same pattern.

// bridge: Both are external inputs that shape behavior.

50 bridges — all personalized to React

// CHOOSE YOUR WORLD

Five Worlds

Same ML syllabus. Five story universes. Each one makes the problem feel real.

Deep Orbit

LIVE

RNN / LSTM / Signals

Detect anomalies in deep-space telemetry aboard the research vessel Archimedes.

Start Deep Orbit →$79one-time

11 modules · 55 lessons · ~22h

deep-scan.ts

// anomaly detection — your capstone

import { sequential, layers } from '@tensorflow/tfjs'

const model = sequential({

layers: [

layers.lstm({ units: 64 }),

layers.dense({ units: 1 }),

]

});

// ▸ DEEP SCAN online

02Q4 '26

Neon Protocol

CNN / Computer Vision

Hunt rogue surveillance in a cyberpunk city.

03Q1 '27

Signal Ward

NLP / Transformers

Build a clinical NLP assistant under pressure.

04Q2 '27

Nova Canvas

GAN / Diffusion

Create generative art for a digital biennale.

05Q3 '27

Terra Grid

Edge AI / MLOps

Monitor infrastructure at the network edge.

// Module 1 is free. No account needed. Others coming — join waitlists.

What You'll Build

By Module 11, you'll have a real-time anomaly detection system — trained, tested, and deployed entirely in the browser.

// DEEP SCAN — ANOMALY DETECTOR

deep-scan.ts

const model = await tf.loadLayersModel('/deep-scan/model.json');

// Real-time signal classification
const prediction = model.predict(signalTensor);
const isAnomaly = prediction.dataSync()[0] > THRESHOLD;

LIVE— runs at 60fps in your browser

Train

Build and train a neural network from scratch using TensorFlow.js. No Python, no backend, no GPU required.

Detect

Classify real-time signal data as normal or anomalous using your own trained LSTM autoencoder.

Deploy

Ship a production-ready model that runs entirely client-side. Inference at 60fps via WebGL acceleration.

Own it

Every line of code is yours. Auto-commit to GitHub. Add it to your portfolio. Show it in interviews.

// module 1 · free · no account required

Bridge
the gap.

Module 1 is completely free. No account, no card, no setup. Just open the lesson and start reading.

Start learning now →