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Introduction to TensorFlow Lite

Approx. 2 months

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Brief Introduction

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.

Course Summary

Learn the basics of TensorFlow Lite, a lightweight machine learning framework that enables on-device inference with low latency and a small binary size. This course covers the fundamentals of TensorFlow Lite and shows you how to build and deploy models on mobile and embedded devices.

Key Learning Points

Understand the basics of TensorFlow Lite and its differences from TensorFlow
Learn how to convert TensorFlow models to TensorFlow Lite models
Deploy models on mobile and embedded devices with TensorFlow Lite

Job Positions & Salaries of people who have taken this course might have

- USA: $112,000
- India: ₹1,900,000
- Spain: €41,000
- USA: $112,000
- India: ₹1,900,000
- Spain: €41,000
- USA: $97,000
- India: ₹900,000
- Spain: €29,000
- USA: $112,000
- India: ₹1,900,000
- Spain: €41,000
- USA: $97,000
- India: ₹900,000
- Spain: €29,000
- USA: $87,000
- India: ₹800,000
- Spain: €25,000

Learning Outcomes

Understand how to use TensorFlow Lite for on-device inference
Convert TensorFlow models to TensorFlow Lite models
Deploy TensorFlow Lite models on mobile and embedded devices

Prerequisites or good to have knowledge before taking this course

Familiarity with TensorFlow
Knowledge of machine learning basics

Course Difficulty Level

Intermediate

Course Format

Self-paced
Online
Video-based

Similar Courses

TensorFlow Developer Certificate
Mobile Web Specialist Certification

Related Education Paths

Notable People in This Field

Andrew Ng
Jeff Dean

Related Books

Description

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

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.

Knowledge

Instructor videosLearn by doing exercisesTaught by industry professionals

Outline

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

Summary of User Reviews

Learn the basics of TensorFlow Lite with this comprehensive course on Udacity. Students praise the course for its clear explanations and engaging content.

Key Aspect Users Liked About This Course

Clear explanations

Pros from User Reviews

Engaging content
Good pacing
Hands-on exercises
Responsive instructors

Cons from User Reviews

Some technical difficulties
Lack of advanced topics
Not suitable for experienced users

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