Learn how to deploy complex machine learning models on single board computers, mobile phones, and microcontrollers
- Gain a comprehensive understanding of TinyML’s core concepts.
- Learn how to design your own TinyML applications from the ground up.
- Explore cutting-edge models, hardware, and software platforms for developing TinyML.
TinyML is an innovative technology that empowers small and resource-constrained edge devices with the capabilities of machine learning. If you’re interested in deploying machine learning models directly on microcontrollers, single board computers, or mobile phones without relying on continuous cloud connectivity, this book is an ideal resource for you.
The book begins with a refresher on Python, covering essential concepts and popular libraries like NumPy and Pandas. It then delves into the fundamentals of neural networks and explores the practical implementation of deep learning using TensorFlow and Keras. Furthermore, the book provides an in-depth overview of TensorFlow Lite, a specialized framework for optimizing and deploying models on edge devices. It also discusses various model optimization techniques that reduce the model size without compromising performance. As the book progresses, it offers a step-by-step guidance on creating deep learning models for object detection and face recognition specifically tailored for the Raspberry Pi. You will also be introduced to the intricacies of deploying TensorFlow Lite applications on real-world edge devices. Lastly, the book explores the exciting possibilities of using TensorFlow Lite on microcontroller units (MCUs), opening up new opportunities for deploying machine learning models on resource-constrained devices.
Overall, this book serves as a valuable resource for anyone interested in harnessing the power of machine learning on edge devices.
What you will learn
- Explore different hardware and software platforms for designing TinyML.
- Create a deep learning model for object detection using the MobileNet architecture.
- Optimize large neural network models with the TensorFlow Model Optimization Toolkit.
- Explore the capabilities of TensorFlow Lite on microcontrollers.
- Build a face recognition system on a Raspberry Pi.
- Build a keyword detection system on an Arduino Nano.