TF Data and Deployment - Device-based Models with TensorFlow Lite Part 1/2
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Join us for our 5th adventure on our journey to deep learning and data science in general 🎉 with the TensorFlow: Data and Deployment Specialization
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Course 2 Week 1
Device-based models with TensorFlow Lite
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!
Course 2 Week 2
Running a TF model in an Android App
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!
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