About this Course

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Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Basic understanding of Kotlin and/or Swift

Approx. 10 hours to complete

Suggested: 4 weeks of study, 4-5 hours/week...

English

Subtitles: English

What you will learn

  • Check

    Prepare models for battery-operated devices

  • Check

    Execute models on Android and iOS platforms

  • Check

    Deploy models on embedded systems like Raspberry Pi and microcontrollers

Skills you will gain

TensorFlow LiteMathematical OptimizationMachine LearningTensorflowObject Detection

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Basic understanding of Kotlin and/or Swift

Approx. 10 hours to complete

Suggested: 4 weeks of study, 4-5 hours/week...

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1

Week 1

6 hours to complete

Device-based models with TensorFlow Lite

6 hours to complete
14 videos (Total 40 min), 6 readings, 2 quizzes
14 videos
A few words from Laurence55s
Features and components of mobile AI2m
Architecture and performance3m
Optimization Techniques2m
Saving, converting, and optimizing a model3m
Examples2m
Quantization3m
TF-Select1m
Paths in Optimization1m
Running the models1m
Transfer learning3m
Converting a model to TFLite1m
Transfer learning with TFLite5m
6 readings
Prerequisites10m
Downloading the Coding Examples and Exercises10m
GPU delegates10m
Learn about supported ops and TF-Select10m
Week 1 Wrap up10m
Exercise Description10m
1 practice exercise
Week 1 Quiz
Week
2

Week 2

1 hour to complete

Running a TF model in an Android App

1 hour to complete
15 videos (Total 36 min), 3 readings, 1 quiz
15 videos
Installation and resources2m
Architecture of a model1m
Initializing the Interpreter2m
Preparing the Input1m
Inference and results1m
Code walkthrough3m
Run the App2m
Classifying camera images55s
Initialize and prepare input3m
Demo of camera image classifier4m
Initialize model and prepare inputs1m
Inference and results3m
Demo of the object detection App1m
Code for the inference and results2m
3 readings
Android fundamentals and installation10m
Week 2 Wrap up10m
Description10m
1 practice exercise
Week 2 Quiz
Week
3

Week 3

2 hours to complete

Building the TensorFLow model on IOS

2 hours to complete
22 videos (Total 45 min), 8 readings, 1 quiz
22 videos
A few words from Laurence1m
What is Swift?45s
TerserflowLiteSwift1m
Cats vs Dogs App1m
Taking the initial steps3m
Scaling the image2m
More steps in the process3m
Looking at the App in Xcode5m
What have we done so far and how do we continue?41s
Using the App50s
App architecture1m
Model details1m
Initial steps4m
Final steps1m
Looking at the code for the image classification App4m
Object classification intro30s
TFL detect App53s
App architecture55s
Initial steps58s
Final steps3m
Looking at the code for the object detection model3m
8 readings
Important links10m
Apple’s developer's site 10m
Apple's API10m
More details10m
Camera related functionalities10m
The Coco dataset10m
Week 3 Wrap up10m
Description10m
1 practice exercise
Week 3 Quiz
Week
4

Week 4

2 hours to complete

TensorFlow Lite on devices

2 hours to complete
13 videos (Total 29 min), 7 readings, 1 quiz
13 videos
A few words from Laurence3m
Devices3m
Starting to work on a Raspberry Pi1m
How do we start?2m
Image classification1m
The 4 step process2m
Object detection1m
Back to the 4 step process4m
Raspberry Pi demo2m
Microcontrollers2m
Closing words by Laurence28s
A conversation with Andrew Ng1m
7 readings
Edge TPU models10m
Options to choose from10m
Pre optimized mobileNet10m
Object detection model trained on the coco10m
Suggested links10m
Description10m
Wrap up10m
1 practice exercise
Week 4 Quiz
4.6
14 ReviewsChevron Right

Top reviews from Device-based Models with TensorFlow Lite

By SMFeb 5th 2020

excellent course with practical examples on using TensorFlow Lite on Raspberry, Android and iOS

By MRJan 5th 2020

A great course to learn how to implement any Deep Learning models on edge devices.

Instructor

Image of instructor, Laurence Moroney

Laurence Moroney 

AI Advocate
Google Brain
128,129 Learners
8 Courses

Offered by

deeplearning.ai logo

deeplearning.ai

About the TensorFlow: Data and Deployment Specialization

Continue developing your skills in TensorFlow as you learn to navigate through a wide range of deployment scenarios and discover new ways to use data more effectively when training your model. In this four-course Specialization, you’ll learn how to get your machine learning models into the hands of real people on all kinds of devices. Start by understanding how to train and run machine learning models in browsers and in mobile applications. Learn how to leverage built-in datasets with just a few lines of code, use APIs to control how data splitting, and process all types of unstructured data. Apply your knowledge in various deployment scenarios and get introduced to TensorFlow Serving, TensorFlow, Hub, TensorBoard, and more. Industries all around the world are adopting AI. This Specialization from Laurence Moroney and Andrew Ng will help you develop and deploy machine learning models across any device or platform faster and more accurately than ever. This Specialization builds upon skills learned in the TensorFlow in Practice Specialization. We recommend learners complete that Specialization prior to enrolling in TensorFlow: Data and Deployment....
TensorFlow: Data and Deployment

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

More questions? Visit the Learner Help Center.