دانلود کتاب شروع سریع یادگیری ماشین کوچک (TinyML) – یادگیری ماشین برای میکروکنترلرهای آردوینو

In the ever-evolving landscape of technology, we find ourselves at a fascinating intersection where the worlds of machine learning and embedded systems converge. This convergence has given birth to Tiny Machine Learning (often shortened as TinyML®, trademark of the EdgeAI foundation)—a field that brings the power of artificial intelligence to the smallest and most resource-constrained computing devices. Rather than requiring expensive cloud infrastructure or powerful computers, Tiny ML allows sophisticated algorithms to run directly on microcontrollers, enabling a new generation of intelligent devices that can make decisions locally, with minimal power consumption and without Internet connectivity.
For Arduino enthusiasts, TinyML represents both an exciting opportunity and a significant challenge. The Arduino platform has long been beloved for its accessibility, allowing individuals with minimal programming experience to create interactive electronic projects quickly. Now, with TinyML, this accessible platform gains the ability to implement sophisticated machine learning capabilities that were previously beyond reach.
However, the journey into TinyML can be daunting, especially for those who may not have a background in data science or machine learning. Many tutorials and resources assume prior knowledge of machine learning principles or require extensive mathematical understanding, creating a significant barrier to entry. Furthermore, the constraints of microcontroller environments add another layer of complexity. Unlike cloud-based machine learning systems with virtually unlimited resources, TinyML development requires careful optimization to fit within tight memory constraints, limited processing power, and strict energy budgets. Converting and optimizing machine learning models for these environments demands specialized knowledge that bridges the gap between traditional machine learning and embedded systems programming.
This is precisely why this book exists. Tiny Machine Learning Quickstart takes a fundamentally different approach to teaching TinyML. Rather than digging too much into the theoretical and mathematical foundations of data science, we start with working solutions and practical code that you can implement immediately from the very first chapters. While understanding the underlying principles of machine learning is undoubtedly valuable, many Arduino enthusiasts simply want to add intelligent features to their projects without becoming machine learning experts. This book respects that desire by providing ready-to-use code snippets, complete workflows, and practical techniques that deliver results first, with theory introduced only where necessary to support practical understanding.