Udacity

Introduction to TensorFlow Lite (Udacity)

Offered by Udacity,

Learn how to deploy deep learning models on mobile and embedded devices with TensorFlow Lite. Learn how to deploy deep learning models on mobile and embedded devices with TensorFlow Lite. This course was developed by the TensorFlow team and Udacity as a practical approach to model deployment for software developers.

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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.

Go to Class

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