000			02155nam a22002537a 4500
003			EG-NcFUE
005			20251126161309.0
008			251110b ua|||||| |||| 00| 0 eng d
020			_a9781032515816
040			_beng
043			_aua
082		4	_223 _a006.31 ATD
100	1		_eAuthor. _qArif, Tariq M.
245	1		_aDeep Learning for Engineers/ _cTariq M. Arif and Md. Adilur Rahim.
264		1	_aBoca Raton, FL: _bChapman & Hall/CRC, _c2024.
300			_axii, 170 pages : _bcolor illustrations ; _c25 cm.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
500			_aDeep 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.
505			_aChapter 1 ◾ Introduction Chapter 2 ◾ Basics of Deep Learning Chapter 3 ◾ Computer Vision Fundamentals Chapter 4 ◾ Natural Language Processing Fundamentals Chapter 5 ◾ Deep Learning Framework Installation: Pytorch and Cuda Chapter 6 ◾ Case Study I: Image Classification Chapter 7 ◾ Case Study II: Object Detection Chapter 8 ◾ Case Study III: Semantic Segmentation Chapter 9 ◾ Case Study IV: Image Captioning
700	1		_ejoint author. _qRahim, Md Adilur.
942			_2ddc _cBK
999			_c13508 _d13508