Deep Learning on Embedded Systems: A Hands-On Approach Using Jetson Nano and Raspberry Pi

— —

Tariq M. Arif (Weber State University, UT)

Deep Learning on Embedded Systems

A Hands-On Approach Using Jetson Nano and Raspberry Pi

Tariq M. Arif (Weber State University, UT)

$157.95

Hardback

Not in-store but you can order this
How long will it take?

Availability Information

We source books from suppliers in Australia and overseas. For books we don't currently have in stock, the time it takes to get them from our suppliers can vary widely - from a few days to a few months - so we check each book with each supplier to determine the expected time it will take to be supplied to us.

We then advise you accordingly. If the time taken to get any book is too long for you, you can let us know and we will cancel or adjust your order, and refund as required.

To find out the anticipated arrival time for specific items prior to ordering, please contact us by phone or email:

Phone +61 2 9264 3111, or 1800 4 BOOKS (1800 4 26657) if outside Sydney:
option 1 Abbey's Bookshop (Crime, History, Science, Kids & more) • info@abbeys.com.au
option 2 Language Book Centre (ESL & Foreign Languages) • language@abbeys.com.au
option 3 Galaxy Bookshop (Sci-fi, Fantasy, Romance, Graphic Novels) • sf@galaxybooks.com.au

QTY:

English

John Wiley & Sons Inc
27 March 2025

Electronics engineering; Embedded systems; Machine learning; Pattern recognition; Computer vision

Comprehensive, accessible introduction to deep learning for engineering tasks through Python programming, low-cost hardware, and freely available software

Deep Learning on Embedded Systems is a comprehensive guide to the practical implementation of deep learning for engineering tasks through computers and embedded hardware such as Raspberry Pi and Nvidia Jetson Nano. After an introduction to the field, the book provides fundamental knowledge on deep learning, convolutional and recurrent neural networks, computer vision, and basics of Linux terminal and docker engines. This book shows detailed setup steps of Jetson Nano and Raspberry Pi for utilizing essential frameworks such as PyTorch and OpenCV. GPU configuration and dependency installation procedure for using PyTorch is also discussed allowing newcomers to seamlessly navigate the learning curve.

A key challenge of utilizing deep learning on embedded systems is managing limited GPU and memory resources. This book outlines a strategy of training complex models on a desktop computer and transferring them to embedded systems for inference. Also, students and researchers often face difficulties with the varying probabilistic theories and notations found in data science literature. To simplify this, the book mainly focuses on the practical implementation part of deep learning using Python programming, low-cost hardware, and freely available software such as Anaconda and Visual Studio Code. To aid in reader learning, questions and answers are included at the end of most chapters.

Written by a highly qualified author, Deep Learning on Embedded Systems includes discussion on:

Fundamentals of deep learning, including neurons and layers, activation functions, network architectures, hyperparameter tuning, and convolutional and recurrent neural networks (CNNs & RNNs) PyTorch, OpenCV, and other essential framework setups for deep transfer learning, along with Linux terminal operations, docker engine, docker images, and virtual environments in embedded devices Training models for image classification and object detection with classification, then converting trained PyTorch models to ONNX format for efficient deployment on Jetson Nano and Raspberry Pi

Deep Learning on Embedded Systems serves as an excellent introduction to the field for undergraduate engineering students seeking to learn deep learning implementations for their senior capstone or class projects and graduate researchers and educators who wish to implement deep learning in their research.

By: Tariq M. Arif (Weber State University UT)
Imprint: John Wiley & Sons Inc
Country of Publication: United States
ISBN: 9781394269266
ISBN 10: 1394269269
Pages: 256
Publication Date: 27 March 2025
Audience: College/higher education , Professional and scholarly , A / AS level , Undergraduate
Format: Hardback
Publisher's Status: Active

Tariq M. Arif, PhD, is an Associate Professor at WSU since 2019. Prior to that, he worked at the University of Wisconsin, Platteville. His primary research interests include artificial intelligence and genetic algorithms for robotics control, computer vision, and biomedical simulations involving machine learning algorithms. He also worked in the Japanese automobile industry for three and a half years as a CAD/CAE engineer.