Title: Ultra portable Deep Learning with Tensorflow.js Speaker: Zack Akil, Developer Advocate at Google Abstract: Traditional machine learning systems usually require some installation process when you want to run them on your own device, not anymore! Zack will be giving give an overview of TensorFlow.js, which allows you to be training and running ML models on your device by simply visiting a webpage. Bio: Zack is Developer advocate at Google and Central London Data Science Project Nights meetup organizer.

1. TensorFlow.js
@ZackAkil
Build & run machine learning models on webpages
js.tensorflow.org

2. Standard TensorFlow
● App installation required for model
to run on your device
Build Train Test RunDeploy
Developer machine or Cloud Client App or Cloud
● Do all training up-front
● Constant web connection
required for Cloud hosted
models

3. TensorFlow.js
Build Train Test Run
Client Browser
● App installation required for model to
run on your device
● Just visit a webpage!
● Do all training up-front
● You can train a model more in
the browser!
● Constant web connection required for
Cloud hosted models
● Once the page is loaded you’re all set!

4. To the Demos!
https://js.tensorflow.org/

5. github.com/ZackAkil/online-learning-with-tensorflow-js

6. Code for new model
model.add(tf.layers.dense({ units: 3, inputShape: [4] }))
model.add(tf.layers.dense({ units: 2 }))
model.compile({ loss: 'meanSquaredError', optimizer: 'sgd' })
const xtrain = tf.tensor2d(x)
const ytrain = tf.tensor2d(y)
const model = tf.sequential();
model.fit(xtrain, ytrain, { epochs: 200 }).then(() => {
console.log("finished training")
});
model.predict(newX).dataSync() //.dataSync() gets values synchronously

7. github.com/ZackAkil/mnist-draw

8. Code for loaded model
const model = await tf.loadModel('model.json')
model.predict(newX).dataSync() //.dataSync() gets values synchronously
newX = tf.fromPixels(canvas, 1) // 1 is for gray scale, 3 for RGB

9. Thank you
@ZackAkil
js.tensorflow.org