Hands-on TinyML Harness the power of Machine Learning on the edge devices (Rohan Banerjee)（Z-Library）

Author： Rohan Banerjee

Learn how to deploy complex Machine Learning models on single board computers, mobile phones, and microcontrollers. Key Features - 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. Description 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. TinyML is going to be the next big thing in Machine Learning. Major tech giants are heavily investing in standardizing the hardware and software stack. In this book, we cover the basic concepts of TinyML through practical coding examples to enable the