Deep Learning for Engineers

— —

Tariq M. Arif (Weber State University Ogden, UT) Md Adilur Rahim (Louisiana State University Baton Rouge, LA)

Deep Learning for Engineers

Tariq M. Arif (Weber State University Ogden, UT), Md Adilur Rahim (Louisiana State University Baton Rouge, LA)

$98.99

Paperback

Not in-store but you can order this
How long will it take?

Availability Information

We source books from suppliers in Australia and overseas. For books we don't currently have in stock, the time it takes to get them from our suppliers can vary widely - from a few days to a few months - so we check each book with each supplier to determine the expected time it will take to be supplied to us.

We then advise you accordingly. If the time taken to get any book is too long for you, you can let us know and we will cancel or adjust your order, and refund as required.

To find out the anticipated arrival time for specific items prior to ordering, please contact us by phone or email:

Phone +61 2 9264 3111, or 1800 4 BOOKS (1800 4 26657) if outside Sydney:
option 1 Abbey's Bookshop (Crime, History, Science, Kids & more) • info@abbeys.com.au
option 2 Language Book Centre (ESL & Foreign Languages) • language@abbeys.com.au
option 3 Galaxy Bookshop (Sci-fi, Fantasy, Romance, Graphic Novels) • sf@galaxybooks.com.au

QTY:

English

Chapman & Hall/CRC
28 February 2024

Automatic control engineering; Machine learning

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.

By: Tariq M. Arif (Weber State University Ogden UT), Md Adilur Rahim (Louisiana State University Baton Rouge, LA)
Imprint: Chapman & Hall/CRC
Country of Publication: United Kingdom
Dimensions: Height: 234mm, Width: 156mm,
Weight: 310g
ISBN: 9781032515816
ISBN 10: 1032515813
Pages: 158
Publication Date: 28 February 2024
Audience: College/higher education , Professional and scholarly , Primary , Undergraduate
Format: Paperback
Publisher's Status: Active

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).