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# Introduction
## AI for Beginners
Welcome to our comprehensive guide to Tiny Machine Learning (TinyML), where we endeavor to bring a fresh perspective to the rapidly emerging field that straddles the domains of electrical engineering, computer science, and applied data science. This book aims to close the gap between complex machine learning abstractions and real-world applications on small devices, providing both theory enthusiasts and practitioners an end-to-end understanding of TinyML.
## Machine Learning and Deep Learning
We begin with an overall introduction to the field of embedd systems and machine learning. We start by elaborating on the key principles of embedded systems, setting the groundwork for embedded machine learning. Then we pivot our attention to deep learning, focusing specifically on deep learning methods given their representation capacity and overall performance in a variety of tasks, especially when applied to small devices.
## Machine Learning
The book goes on to discuss step-by-step workflows in machine learning, data engineering, pre-processing, and advanced model training techniques. It provides comprehensive analyses of several in-use machine learning frameworks, and how they can be employed effectively to develop efficient AI models.
## Deep Learning
In a world where efficiency is key, we also discuss TinyML model optimization and deployment strategies. Special focus is given to on-device learning. How do we train a machine learning model on a tiny device while achieving admirable efficiency? What are the current hardware acceleration techniques? And how can we manage the lifecycle of these models? The reader can expect exhaustive answers to these and many more questions in our dedicated chapters.
## Applications of Deep Learning
Importantly, we adopt a forward-looking stance, discussing the sustainability and ecological footprint of AI. We explore the location of TinyML within such debates, and how TinyML may contribute to more sustainable and responsible practices.
## Quiz
Finally, the book ends with a speculative leap into the world of generative AI, outlining its potentials in the TinyML context.
Whether you are an absolute beginner, a professional in the field, or an academic pursuing rigorous research, this book aims to offer a seamless blend of essential theory and practical insight, triggering stimulating conversations around TinyML. Let's embark on this thrilling journey to explore the incredible world of TinyML!