"Preface The field of Artificial intelligence has undergone a significant transformation in the last decade, moving from traditional machine learning approaches to more sophisticated deep learning techniques. This evolution has brought extraordinary advancements across various industries, including healthcare, finance, transportation, manufacturing, robotics, and consumer technology. For this reason, there is a growing need to incorporate deep learning technology in various research projects and academic curricula. As ...
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"Preface The field of Artificial intelligence has undergone a significant transformation in the last decade, moving from traditional machine learning approaches to more sophisticated deep learning techniques. This evolution has brought extraordinary advancements across various industries, including healthcare, finance, transportation, manufacturing, robotics, and consumer technology. For this reason, there is a growing need to incorporate deep learning technology in various research projects and academic curricula. As customizable embedded devices become more affordable and portable for deploying AI models, the growing demand for exploring this technology is also spreading across all age groups, from children to the elderly. This book aims to address this demand and serves as a comprehensive hands-on guide to understanding the integration of deep learning with modern embedded systems, such as Jetson Nano and Raspberry Pi. It also focuses on the key components of deep learning models in simple terms without diving deeply into the statistical or mathematical theories behind them. A basic understanding of Python programming is necessary to follow the examples, as all the programs in this book are written in Python. The book introduces key concepts of deep learning and its architectures in chapters 2 and 3. Chapter 4 includes the configuration of the Windows PC used for setting up PyTorch and its related packages. This chapter also explains basic tensor operations using PyTorch. Chapter 5 and Chapter 13 include Jetson Nano and Raspberry Pi 5 configurations, respectively, along with the list of peripherals used for deploying deep learning models. As the operation of Jetson Nano and Raspberry Pi 5 involves using Linux terminals, Chapter 6 covers basic Linux terminal commands, focusing on file management and permissions. This chapter will be beneficial for readers who are unfamiliar with the Linux operating system. Chapter 7 presents the fundamentals of setting up the Docker engines and building Docker images, and demonstrates how to perform model inference within Jetson's Docker container. Chapter 11 explains how to create a deep-learning dataset for image classification and object detection using bounding boxes. The dataset developed in this chapter is utilized for model training in Chapters 9 and 10. Chapter 9 outlines the process for training a classification model, while Chapter 10 demonstrates the approach for training an object detection model with image classification"--
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