Introduction to TensorFlow Lite (Udacity)

You'll get hands-on experience with the TensorFlow Lite framework as you deploy deep learning models on Android, iOS, and even an embedded Linux platform. By the end of this course, you'll have all the skills necessary to start deploying your own deep learning models into your apps.
With TensorFlow Lite, the Google TensorFlow team has introduced the next evolution of the TensorFlow Framework, specifically designed to enable machine learning at low latency on mobile and embedded devices. This course was created as a practical approach to model deployment for software developers, providing hands-on experience deploying deep learning models on Android, iOS, and even an embedded Linux platform. Get started today to stay on the cutting-edge of machine learning practices.

What You Will Learn

Lesson 1
Introduction to TensorFlow Lite

• Learn how TensorFlow works under the hood
• Learn how to quantize models
• Learn how to test your TF Lite Models in Python

Lesson 2
TensorFlow Lite on Android

• Deploy a TF Lite Model to an Android app that classifies images of cats and dogs
• Deploy a TF Lite Model to an Android app that classifies images of various objects
• Deploy a TF Lite Model to an Android app that performs object detection
• Deploy a TF Lite Model to an Android app that recognizes speech commands

Lesson 3
TensorFlow Lite on Swift

• Deploy a TF Lite Model to an iOS app that classifies images of cats and dogs
• Deploy a TF Lite Model to an iOS app that classifies images of various objects
• Deploy a TF Lite Model to an iOS app that performs object detection
• Deploy a TF Lite Model to an iOS app that recognizes speech commands

Lesson 4
TensorFlow Lite on IoT

• Deploy a TF Lite Model to a Linux embedded platform that classifies images of cats and dogs
• Deploy a TF Lite Model to a Linux embedded platform that classifies images of various objects
• Deploy a TF Lite Model to a Linux embedded platform that performs object detection

Prerequisites and Requirements
General Experience: Some familiarity with the TensorFlow Lite framework, and comfortability with Object Oriented Programming, Python, Swift, Android, and Machine Learning.