Deep Learning for Engineers Paperback / softback
by Tariq M. (Weber State University Ogden, UT) Arif, Md Adilur (Louisiana State University Baton Rouge, LA) Rahim
Paperback / softback
Description
Deep Learning for Engineers introduces the fundamental principles of deep learning along with an explanation of the 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.
Information
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Less than 10 available - usually despatched within 24 hours
- Format:Paperback / softback
- Pages:158 pages, 14 Line drawings, color; 117 Halftones, color; 131 Illustrations, color
- Publisher:Taylor & Francis Ltd
- Publication Date:28/02/2024
- Category:
- ISBN:9781032515816
Information
-
Less than 10 available - usually despatched within 24 hours
- Format:Paperback / softback
- Pages:158 pages, 14 Line drawings, color; 117 Halftones, color; 131 Illustrations, color
- Publisher:Taylor & Francis Ltd
- Publication Date:28/02/2024
- Category:
- ISBN:9781032515816
