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Learner Reviews & Feedback for Device-based Models with TensorFlow Lite by deeplearning.ai

4.5
stars
23 ratings
4 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....

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1 - 6 of 6 Reviews for Device-based Models with TensorFlow Lite

By Mo R

Jan 05, 2020

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

By Marco A P N

Jan 17, 2020

Awesome. I learned a lot

By Igor M

Jan 07, 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 Michael

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 Bourgoin C

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 Desiré D W

Dec 25, 2019

Great course, content and instructor, but the assignment had some issues.

I submitted over 10 times without any feedback other then 'Grader Malfunction'. Also, all those times, the test cells in the notebook (put together by the instructors) ran smoothly, leaving my in the dark how to fix it. Was a bit of a frustrating experience.

Other than that, great content, very condense, very valuable to know how to deploy models on apps or small machines. This course inspired me to learn more about robotics - to apply ML on physical projects.