TensorFlow: Data and Deployment Specialization

1. Browser-based Models with TensorFlow.js

2. Device-based Models with TensorFlow Lite

3. Data Pipelines with TensorFlow Data Services

4. Advanced Deployment Scenarios with TensorFlow

6 hours to complete

Device-based models with TensorFlow Lite

1 hour to complete

Running a TF model in an Android App

2 hours to complete

Building the TensorFLow model on IOS

2 hours to complete

TensorFlow Lite on devices

basic understanding of Kotlin and/or Swift, as well as Android Studio and/or Xcode, will help you follow along.


lightweight+low-latency+privacy+improved power consumption+efficient model ready to used


  • All available CPU platforms are supported

  • Reducing latency and inference cost

  • Low memory footprint

  • Allow execution on hardware restricted-to or optimize for fixed-point operations

  • Optimized models for special purpose HW accelerator TUP

Weight pruning

Model topology transforms

Tensor decomposition


Chrome as our internet browser, Brackets as our HTML editor and the Web Server for Chrome App as our web server.

TF.js: training and inference on browser

  1. Chrome

  2. https://chrome.google.com/webstore/detail/web-server-for-chrome/ofhbbkphhbklhfoeikjpcbhemlocgigb?hl=en

  3. http://brackets.io/

  4. https://github.com/lmoroney/dlaicourse




Android (cat and dog)

mobileNet classification android

MobileNet ssd up to 10 objects from 80 classes



Tf.tidy() -> save memory




Converting Models to JavaScript

Install Wget on Mac/Linux

1. Install Homebrew by running the following command in your terminal:

$ /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

2. Install wget byrunning the following command in your terminal:

$ brew install wget

Install Wget on Windows

  1. Go to https://eternallybored.org/misc/wget/

  2. Download the wget.exe file from the links provided. You can download the latest version of wget for either 32-bit or 64-bit systems.

  3. If prompted, click Run or Save.

  4. If you chose Save, double-click the downloaded file to start installing.


Transfer learning mobileNet