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Etat : New.

Paperback. Etat : New. Brand new book, sourced directly from publisher. Dispatch time is 7-8 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Etat : As New. Unread book in perfect condition.

Etat : New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Etat : New. pp. 336 1st edition NO-PA16APR2015-KAP.

Etat : New. pp. 336.

Etat : New. pp. 336.

Etat : New.

Etat : New.

Etat : As New. Unread book in perfect condition.

Paperback. Etat : New. 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. …

Paperback. Etat : Brand New. 192 pages. 9.19x6.13x0.39 inches. In Stock.

Hardcover. Etat : New. Brand new book, sourced directly from publisher. Dispatch time is 7-8 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : New. 1st edition NO-PA16APR2015-KAP.

Etat : New.

Etat : New.

Paperback. Etat : New. 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. …

Hardcover. Etat : Brand New. 192 pages. 9.19x6.13x0.59 inches. In Stock.

Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 t.…

Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 t.…