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.
Device-based Models with TensorFlow Lite
Device-based Models with TensorFlow Lite
This course is part of TensorFlow: Data and Deployment Specialization
Instructor: Laurence Moroney
32,137 already enrolled
Gain insight into a topic and learn the fundamentals.
4.7
(653 reviews)
Intermediate level
Recommended experience
Flexible schedule
Approx. 10 hours
Learn at your own pace
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What you'll learn
Prepare models for battery-operated devices
Execute models on Android and iOS platforms
Deploy models on embedded systems like Raspberry Pi and microcontrollers
Skills you'll gain
Details to know
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Assessments
4 assignments
Taught in English
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This course is part of the TensorFlow: Data and Deployment Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
Welcome to this course on TensorFlow Lite, an exciting technology that allows you to put your models directly and literally into people's hands. You'll start with a deep dive into the technology, and how it works, learning about how you can optimize your models for mobile use -- where battery power and processing power become an important factor. You'll then look at building applications on Android and iOS that use models, and you'll see how to use the TensorFlow Lite Interpreter in these environments. You'll wrap up the course with a look at embedded systems and microcontrollers, running your models on Raspberry Pi and SparkFun Edge boards. Don't worry if you don't have access to the hardware -- for the most part you'll be able to do everything in emulated environments. So, let's get started by looking at what TensorFlow is and how it works!
What's included
14 videos8 readings1 assignment1 programming assignment1 ungraded lab
Last week you learned about TensorFlow Lite and you saw how to convert your models from TensorFlow to TensorFlow Lite format. You also learned about the standalone TensorFlow Lite Interpreter which could be used to test these models. You wrapped with an exercise that converted a Fashion MNIST based model to TensorFlow Lite and then tested it with the interpreter. This week you'll look at the first of the deployment types for this course: Android. Android is a versatile operating system that is used in a number of different device type, but most commonly phones, tablets and TV systems. Using TensorFlow Lite you can run your models on Android, so you can bring ML to any of these device types. While it helps to understand some Android programming concepts, we hope that you'll be able to follow along even if you don't, and at the very least try out the full sample apps that we'll explore for Image Classification, Object Detection and more!
What's included
15 videos4 readings1 assignment
The other popular mobile operating system is, of course, iOS. So this week you'll do very similar tasks to last week -- learning how to take models and run them on iOS. You'll need some programming background with Swift for iOS to fully understand everything we go through, but even if you don't have this expertise, I think this weeks content is something you'll find fun to explore -- and you'll learn how to build a variety of ML applications that run on this important operating system!
What's included
22 videos9 readings1 assignment
Now that you've looked at TensorFlow Lite and explored building apps on Android and iOS that use it, the next and final step is to explore embedded systems like Raspberry Pi, and learn how to get your models running on that. The nice thing is that the Pi is a full Linux system, so it can run Python, allowing you to either use the full TensorFlow for Training and Inference, or just the Interpreter for Inference. I'd recommend the latter, as training on a Pi can be slow!
What's included
13 videos9 readings1 assignment
Instructor
Instructor ratings
4.8 (75 ratings)Offered by
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4.7
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Showing 3 of 653
SK
5
Reviewed on Jul 15, 2020
Laurence is a good teacher. His explanations are clear and to the point. This is a one of the best sources to learn how to deploy ML models on devices
AC
4
Reviewed on Apr 10, 2020
The course was a good one from the instructor. Could have made it more interesting. But anyways a good starter course for anyone.
BS
5
Reviewed on 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 deeplearning.ai for this kind of courses.
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