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Deep Learning for Engineers PDF

170 Pages·2024·19 MB·English
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by Tariq M. Arif| 2024| 170 pages| 19| English

About Deep Learning for Engineers

Deep Learning for Engineers introduces fundamental principles of deep learning along with the explanation of basic elements required for understanding and applying deep learning models. As a comprehensive guideline for applying deep learning models in practical settings, this book features an easy-to-understand coding structure using Python and PyTorch with an in-depth explanation of four typical deep learning case studies on image classification, object detection, semantic segmentation, and image captioning. The fundamentals of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) architectures and their practical implementations in science and engineering are also discussed. This book includes exercise problems for all case studies focusing on various fine-tuning approaches in deep learning. Science and engineering students at both undergraduate and graduate levels, academic researchers, and industry professionals will find the contents useful.

Detailed Information

Author:Tariq M. Arif
Publication Year:2024
ISBN:9781032504735
Pages:170
Language:English
File Size:19
Format:PDF
Price:FREE
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