Deep Learning for Engineers/ (Record no. 13508)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02155nam a22002537a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | EG-NcFUE |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20251126161309.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 251110b ua|||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781032515816 |
| 040 ## - CATALOGING SOURCE | |
| Language of cataloging | eng |
| 043 ## - GEOGRAPHIC AREA CODE | |
| Geographic area code | ua |
| 082 #4 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Edition number | 23 |
| Classification number | 006.31 ATD |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Relator term | Author. |
| Fuller form of name | Arif, Tariq M. |
| 245 1# - TITLE STATEMENT | |
| Title | Deep Learning for Engineers/ |
| Statement of responsibility, etc | Tariq M. Arif and Md. Adilur Rahim. |
| 264 #1 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc | Boca Raton, FL: |
| Name of publisher, distributor, etc | Chapman & Hall/CRC, |
| Date of publication, distribution, etc | 2024. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xii, 170 pages : |
| Other physical details | color illustrations ; |
| Dimensions | 25 cm. |
| 336 ## - CONTENT TYPE | |
| Source | rdacontent |
| Content type term | text |
| 337 ## - MEDIA TYPE | |
| Source | rdamedia |
| Media type term | unmediated |
| 338 ## - CARRIER TYPE | |
| Source | rdacarrier |
| Carrier type term | volume |
| 500 ## - GENERAL NOTE | |
| General note | 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.<br/><br/>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.<br/><br/>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 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Chapter 1 ◾ Introduction<br/>Chapter 2 ◾ Basics of Deep Learning<br/><br/>Chapter 3 ◾ Computer Vision Fundamentals<br/><br/>Chapter 4 ◾ Natural Language Processing Fundamentals<br/><br/>Chapter 5 ◾ Deep Learning Framework Installation: Pytorch and Cuda<br/><br/>Chapter 6 ◾ Case Study I: Image Classification<br/><br/>Chapter 7 ◾ Case Study II: Object Detection<br/><br/>Chapter 8 ◾ Case Study III: Semantic Segmentation<br/><br/>Chapter 9 ◾ Case Study IV: Image Captioning |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Relator term | joint author. |
| Fuller form of name | Rahim, Md Adilur. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Books |
| Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Date acquired | Source of acquisition | Total Checkouts | Full call number | Barcode | Date last seen | Copy number | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | Computers & Information Technology ( General ) | Main library | Main library | 10/11/2025 | Baccah | 006.31 ATD | 00017696 | 10/11/2025 | C.1 | 10/11/2025 | Books |