{"title":"Tariq M. Arif","description":"\u003cp\u003eTariq M. Arif specialises in the intersection of engineering and advanced technology, with a particular focus on the application of deep learning in embedded systems. His work delves into practical methods and theoretical insights, offering readers a sophisticated understanding of cutting-edge innovations.\u003c\/p\u003e\n\n\u003cp\u003ePerfect for professionals and enthusiasts alike, Arif’s books explore the challenges and opportunities presented by integrating artificial intelligence into hardware, providing clear guidance on emerging techniques that shape the future of technology.\u003c\/p\u003e","products":[{"product_id":"deep-learning-on-embedded-systems-by-tariq-m-arif-9781394269266","title":"Deep Learning on Embedded Systems","description":"\u003cdiv class=\"book-description\"\u003e\n\u003cp\u003e\u003cstrong\u003eComprehensive, accessible introduction to deep learning for engineering tasks through Python programming, low-cost hardware, and freely available software\u003c\/strong\u003e\u003c\/p\u003e\n\n\u003cp\u003e\u003cem\u003eDeep Learning on Embedded Systems\u003c\/em\u003e 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 for Jetson Nano and Raspberry Pi to utilize essential frameworks such as PyTorch and OpenCV. GPU configuration and dependency installation procedures for using PyTorch are also discussed, allowing newcomers to seamlessly navigate the learning curve.\u003c\/p\u003e\n\n\u003cp\u003eA 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. 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.\u003c\/p\u003e\n\n\u003cp\u003eWritten by a highly qualified author, \u003cem\u003eDeep Learning on Embedded Systems\u003c\/em\u003e includes discussion on:\u003c\/p\u003e\n\n\u003cul\u003e\n  \u003cli\u003eFundamentals of deep learning, including neurons and layers, activation functions, network architectures, hyperparameter tuning, and convolutional and recurrent neural networks (CNNs \u0026amp; RNNs)\u003c\/li\u003e\n  \u003cli\u003ePyTorch, 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\u003c\/li\u003e\n  \u003cli\u003eTraining 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\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003cp\u003e\u003cem\u003eDeep Learning on Embedded Systems\u003c\/em\u003e 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.\u003c\/p\u003e\n\u003c\/div\u003e","brand":"Unknown","offers":[{"title":"Default Title","offer_id":47470400078060,"sku":"9781394269266","price":180.99,"currency_code":"NZD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0705\/7784\/8556\/files\/9781394269266-deep-learning-on-embedded-systems.jpg?v=1775218401"}],"url":"https:\/\/bookhero.co.nz\/collections\/tariq-m-arif.oembed","provider":"Book Hero","version":"1.0","type":"link"}