Device-based Models with TensorFlow Lite

Go to Course: https://www.coursera.org/learn/device-based-models-tensorflow

Introduction

### Course Review: Device-based Models with TensorFlow Lite **Overview** In today's rapidly evolving tech landscape, the ability to deploy machine learning models onto portable devices is becoming increasingly crucial. The course “Device-based Models with TensorFlow Lite” on Coursera provides an excellent opportunity for those looking to bridge the gap between theoretical machine learning and practical application on mobile platforms. This course is part of a specialization designed to guide students in effectively translating their machine learning models into real-world applications, particularly in the context of mobile and embedded systems. **Course Content** The course is structured to ease learners into the world of TensorFlow Lite, a lightweight version of TensorFlow specifically designed for mobile and embedded devices. Here’s a closer look at the syllabus: 1. **Introduction to TensorFlow Lite**: The course begins with an in-depth exploration of the technology behind TensorFlow Lite, emphasizing the optimization required for running models on lower-powered devices. Here, you will learn about the importance of battery life and processing power, preparing you to face the challenges of model deployment in a mobile environment. 2. **Running Models on Android**: This section introduces students to deploying machine learning models on Android devices. With Android being a prevalent operating system across a wide variety of device types, this module will help you understand the essentials of Android programming while providing hands-on experience with sample applications for image classification and object detection. 3. **Building Models for iOS**: Similar to the Android module, this week focuses on deploying models on iOS, where Swift programming knowledge is beneficial. However, the course content is structured to be approachable for beginners, making it enjoyable and informative regardless of your programming background. You will learn to build different ML applications that can run on Apple devices. 4. **TensorFlow Lite on Embedded Systems**: The final module takes a broader approach, exploring how to run your models on embedded systems such as Raspberry Pi. This part of the course is particularly exciting, as it underscores the versatility of TensorFlow Lite in environments beyond mobile applications. Here, you will focus on running inference using the TensorFlow Lite interpreter, enabling you to apply your knowledge to practical projects without needing extensive hardware. **Recommendation** "Device-based Models with TensorFlow Lite" is highly recommended for anyone interested in the field of mobile machine learning. The course is particularly suited for: - **Data Scientists and Machine Learning Engineers**: If your goal is to transition your models into mobile applications, this course equips you with the practical skills needed for deployment. - **Software Developers**: Developers who are interested in integrating machine learning into their applications will find the course content valuable, especially the sections dedicated to Android and iOS apps. - **Beginners to Embedded Systems**: The end of the course provides a fantastic introduction to working with embedded systems, making it a good starting point for anyone who wants to explore this area further. **Conclusion** This course on Coursera is meticulously crafted to ensure that learners not only understand TensorFlow Lite but also gain hands-on experience in deploying models across various platforms. With a blend of theoretical knowledge and practical application, you'll be able to confidently bring machine learning models into real-world scenarios. Whether you're a seasoned practitioner or just starting, "Device-based Models with TensorFlow Lite" promises to enhance your skillset and broaden your horizons in the exciting field of mobile machine learning. So, gear up and dive into the world of TensorFlow Lite—your journey towards making smart devices even smarter starts here!

Syllabus

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!

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!

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!

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!