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Back to Device-based Models with TensorFlow Lite

Learner Reviews & Feedback for Device-based Models with TensorFlow Lite by DeepLearning.AI

548 ratings
92 reviews

About the Course

Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model. This second course teaches you how to run your machine learning models in mobile applications. You’ll learn how to prepare models for a lower-powered, battery-operated devices, then execute models on both Android and iOS platforms. Finally, you’ll explore how to deploy on embedded systems using TensorFlow on Raspberry Pi and microcontrollers. This Specialization builds upon our TensorFlow in Practice Specialization. If you are new to TensorFlow, we recommend that you take the TensorFlow in Practice Specialization first. To develop a deeper, foundational understanding of how neural networks work, we recommend that you take the Deep Learning Specialization....

Top reviews


Mar 24, 2021

Great course - I learned a lot about how TensorFlow can be run on a wide variety of devices. I am especially interested in TensorFlow running on Raspberry Pi, Google Dev Board (Coral) and Jetson Nano.


Oct 12, 2020

Really informative course on tf lite for beginners like me, it has given serious thoughts about the EDGEML field and opportunities , thanks coursera and for this kind of courses.

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51 - 75 of 94 Reviews for Device-based Models with TensorFlow Lite

By Shakib K

Dec 28, 2020

Awesome and useful course, Thanks Laurence.

By Kamlesh C

Aug 7, 2020

Thanks, I learned a lot from this course.

By Yilber R

Feb 4, 2022

Muy intuitivo y el instructor super

By Ricky A

Apr 21, 2022


By Mellania P S

May 9, 2021

Amazing, Great and awesome course

By Ventseslav V

May 1, 2020

Thanks, it was quality material

By Cheuk L Y

Jun 26, 2020

Cool intro to all the devices

By Pablo G G

Feb 4, 2021

Muy bien explicado.

By Kasun

Oct 23, 2020

loving the content

By amadou d

May 7, 2021


By Muhammad T

Apr 27, 2021

good course


Jul 18, 2020

nice course

By Bintang F E

May 28, 2021


By Levina A

May 21, 2021

so cool

By Muhammad N I

Apr 24, 2022


By Ahmad H N

Apr 28, 2021


By 林韋銘

Jun 12, 2020


By AasaiAlangaram

Mar 6, 2020

Very Useful course for me. I enjoyed go through first 3 week materials. Then comes the week 4 which is my favorite part because in which we learn about using tensorflow-lite in edge computing devices like raspberry pi, sparkfun edge modele. Expecting Much more from like this one.

By Deleted A

Jul 4, 2020

Good course. Primer into android and raspberry pi is great, not familiar with IOS development but seemed very lengthy compared to even android. TFLite seems great. Haven't taken the mobile apps for a ride yet, but will do it soon.

By Igor M

Jan 7, 2020

This course provided useful information on device specific implementation of TFlite. With an interesting optional assignments, though the assignments are the same with just some small differences in implementation.

By Guillermo P T

Apr 12, 2020

It's a very instructive course by I missed more detailed explanations, at least the more basic, for building android projects and a little guidence for setting up the whole project step by step.


Apr 17, 2020

Quite good course. It gives an opportunity for individuals to utilize tensor flow in day to day devices which makes it more appealing. Thanks for developing this course.

By Michael M

Jan 12, 2020

Great course, very practical in the real world. It also balances and accommodates developers on what devices you have available. Looking forward to the next course

By Christophe B

Jan 17, 2020

Interesting course on how to use Tensorflow Lite on mobile phone or raspberry. More projects & sometimes more explanations about configuration would be necessary.

By John U

Apr 3, 2021

Good introduction into getting TensorFlow models up and running on different platforms from microcontrollers, raspberry PI through to IOS and Android