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Learn moreIntroduction to TensorFlow Lite
Free Course
Learn how to deploy deep learning models on mobile and embedded devices with TensorFlow Lite.
Estimated time
Approx. 2 Months
Skill level
Intermediate
Prerequisites
See prerequisites in detail
In collaboration with
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.
What you will learn
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
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
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
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.
Learn with the best.
Daniel Situnayake
Developer Advocate, Google
Paige Bailey
Developer Advocate, Google
Juan Delgado
Content Developer, Udacity