Arduino V Machine Learning
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Arduino V: Machine Learning
This book is about the Arduino microcontroller and the Arduino concept. The visionary Arduino represented a new innovation in microcontroller hardware in 2005, the concept of open source hardware, making a broad range of computing accessible for all. This book, “Arduino V: AI and Machine Learning,” is an accessible primer on Artificial Intelligence and Machine Learning for those without a deep AI and ML background. The author concentrates on Artificial Intelligence (AI) and Machine Learning (ML) applications for microcontroller–based systems. The intent is to introduce the concepts and allow readers to practice on low cost, accessible Arduino hardware and software. Readers should find this book a starting point, an introduction, to this fascinating field. A number of references are provided for further exploration.
TinyML
Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size
Machine Learning Approaches and Applications in Applied Intelligence for Healthcare Data Analytics
In the last two decades, machine learning has developed dramatically and is still experiencing a fast and everlasting change in paradigms, methodology, applications and other aspects. This book offers a compendium of current and emerging machine learning paradigms in healthcare informatics and reflects on their diversity and complexity. Machine Learning Approaches and Applications in Applied Intelligence for Healthcare Data Analytics presents a variety of techniques designed to enhance and empower multi-disciplinary and multi-institutional machine learning research. It provides many case studies and a panoramic view of data and machine learning techniques, providing the opportunity for novel insights and discoveries. The book explores the theory and practical applications in healthcare and includes a guided tour of machine learning algorithms, architecture design and interdisciplinary challenges. This book is useful for research scholars and students involved in critical condition analysis and computation models.