Free Course

Introduction to TensorFlow Lite

by
TensorFlow Lite

Learn how to deploy deep learning models on mobile and embedded devices with TensorFlow Lite.

About this Course

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. 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.

Course Cost
Free
Timeline
Approx. 2 months
Skill Level
intermediate
Included in Product

Rich Learning ContentRich Learning Content

Interactive QuizzesInteractive Quizzes

Taught by Industry ProsTaught by Industry Pros

Self-Paced LearningSelf-Paced Learning

Student Support CommunityStudent Support Community

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Introduction to TensorFlow Lite

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Course Leads

Daniel Situnayake

Daniel Situnayake

Developer Advocate, Google

Paige Bailey

Paige Bailey

Developer Advocate, Google

Juan Delgado

Juan Delgado

Content Developer, Udacity

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.

See the Technology Requirements for using Udacity.

Why Take This Course

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 do I get?
  • Instructor videos
  • Learn by doing exercises
  • Taught by industry professionals