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Device-based Models with TensorFlow Lite(으)로 돌아가기

deeplearning.ai의 Device-based Models with TensorFlow Lite 학습자 리뷰 및 피드백

555개의 평가
92개의 리뷰

강좌 소개

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....

최상위 리뷰


2021년 3월 24일

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.


2020년 10월 12일

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.

필터링 기준:

Device-based Models with TensorFlow Lite의 94개 리뷰 중 51~75

교육 기관: Shakib K

2020년 12월 28일

Awesome and useful course, Thanks Laurence.

교육 기관: Kamlesh C

2020년 8월 7일

Thanks, I learned a lot from this course.

교육 기관: Yilber R

2022년 2월 4일

Muy intuitivo y el instructor super

교육 기관: Ricky A

2022년 4월 21일


교육 기관: Mellania P S

2021년 5월 9일

Amazing, Great and awesome course

교육 기관: Ventseslav V

2020년 5월 1일

Thanks, it was quality material

교육 기관: Cheuk L Y

2020년 6월 26일

Cool intro to all the devices

교육 기관: Pablo G G

2021년 2월 4일

Muy bien explicado.

교육 기관: Kasun

2020년 10월 23일

loving the content

교육 기관: amadou d

2021년 5월 7일


교육 기관: Muhammad T

2021년 4월 27일

good course

교육 기관: RAGHAV N

2020년 7월 18일

nice course

교육 기관: Bintang F E

2021년 5월 28일


교육 기관: Levina A

2021년 5월 21일

so cool

교육 기관: Muhammad N I

2022년 4월 24일


교육 기관: Ahmad H N

2021년 4월 28일


교육 기관: 林韋銘

2020년 6월 12일


교육 기관: AasaiAlangaram

2020년 3월 6일

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.

교육 기관: Deleted A

2020년 7월 4일

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.

교육 기관: Igor M

2020년 1월 7일

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.

교육 기관: Guillermo P T

2020년 4월 12일

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.


2020년 4월 17일

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.

교육 기관: Michael M

2020년 1월 12일

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

교육 기관: Christophe B

2020년 1월 17일

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

교육 기관: John U

2021년 4월 3일

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