Deep Learning for Engineers - Arif, Tariq M; Rahim, Adilur
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Introduces fundamental principles of deep learning with the explanation of basic elements required for understanding and applying deep learning models. Features coding structure using Python and PyTorch, presenting four typical deep learning case studies on image classification, object detection, semantic segmentation, and image captioning.

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Produktbeschreibung
Introduces fundamental principles of deep learning with the explanation of basic elements required for understanding and applying deep learning models. Features coding structure using Python and PyTorch, presenting four typical deep learning case studies on image classification, object detection, semantic segmentation, and image captioning.
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Autorenporträt
Tariq M. Arif is an assistant professor in the Department of Mechanical Engineering at Weber State University, UT. Prior to that, he worked at the University of Wisconsin, Platteville, as a lecturer faculty. Tariq obtained his Ph.D. in 2017 from the Mechanical Engineering Department of New Jersey Institute of Technology (NJIT), NJ. His main research interests are in the area of artificial intelligence and genetic algorithms for robotics control, computer vision, and biomedical simulations of focused ultrasound. He completed his Masters in 2011 from the University of Tokushima, Japan, and B.Sc. in 2005 from Bangladesh University of Engineering and Technology (BUET). Md Adilur Rahim is an accomplished engineer and researcher specializing in flood hazard characterization, risk assessment, and the application of advanced data analysis and deep learning techniques. Currently, he is working as a postdoctoral researcher at the Louisiana State University, AgCenter. He achieved his Ph.D. in Engineering Science in the summer of 2023 and M.Sc. in Civil Engineering in the spring of 2022 from Louisiana State University, LA. Earlier, in 2014, he graduated with a B.Sc. in Civil Engineering from the Bangladesh University of Engineering & Technology (BUET).