index.md

templateKey	heading	subheading	mainpitch	intro	model	acknowledgements	team
home-page	Friendly Machine Learning for the Web	A neighborly approach to creating and exploring artificial intelligence in the browser.	title description Why ml5.js? ml5.js aims to make machine learning approachable for a broad audience of artists, creative coders, and students. The library provides access to machine learning algorithms and models in the browser, building on top of TensorFlow.js with no other external dependencies.	blurbs image text ./assets/2000px-TensorFlowLogo-branded.png ml5.js is an open source, friendly high level interface to TensorFlow.js, a library for handling GPU-accelerated mathematical operations and memory management for machine learning algorithms. image text ./assets/imageclassification-demo-bg.png ml5.js provides immediate access in the browser to pre-trained models for detecting human poses, generating text, styling an image with another, composing music, pitch detection, and common English language word relationships, and much more. image text ./assets/everyday-ai-mimi.png ml5.js aims to support broader public understanding of machine learning and foster deeper engagement with ethical computing, responsible data collection, and accessibility and diversity of people and perspectives in technology and the arts.	blurbs heading image text ./assets/ref-posenet.png PoseNet is a machine learning model that allows for Real-time Human Pose Estimation. image text ./assets/ref-yolo.png YOLO (You only look once) is a state-of-the-art, real-time object detection and classification system. image text ./assets/ref-styletransfer.png pix2pix is image-to-image translation with conditional adversarial networks. image text ./assets/ref-classification.png Classify the content of images with pre-trained models. image text ./assets/ref-sketchrnn.png Generate new doodles with a neural network based on Google's Quick Draw. Discover the creative possibilities of machine learning!	blurbs heading image text ./assets/acknowledgements__google-research.png Google Faculty Research Grant image text ./assets/acknowledgements__itp-logo.png NYU ITP image text ./assets/acknowledgements__processing.png Processing Foundation image text ./assets/acknowledgements__engelberg-center.jpg NYU Engelberg Center image text ./assets/acknowledgements__STUDIO.jpg Frank-Ratchye STUDIO image text ./assets/acknowledgements__cosaSticker.jpg COSA image text ./assets/acknowledgements__netlify.png Netlify image text ./assets/acknowledgements__browserstack.png Browserstack ml5.js has received support and funding from these generous folks	heading image profile Meet our team! ./assets/logo-purple-circle.png ml5.js is an open source project developed and maintained by NYU's Interactive Telecommunications/Interactive Media Arts program and by artists, designers, students, technologists, and developers from all over the world.

ml5.js provides an approachable API and examples to help you get started

// Step 1: Create an image classifier with MobileNet
const classifier = ml5.imageClassifier("MobileNet", onModelReady);

// Step 2: select an image
const img = document.querySelector("#myImage");

// Step 3: Make a prediction
let prediction = classifier.predict(img, gotResults);

// Step 4: Do something with the results!
function gotResults(err, results) {
  console.log(results);
  // all the amazing things you'll add
}