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Автор: Tariq M. Arif
Издательство: Wiley
Год: 2025
Страниц: 256
Язык: английский
Формат: epub (true)
Размер: 66.5 MB
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. This book outlines a strategy of training complex models on a desktop computer and transferring them to embedded systems for inference. 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.