Introduction to Deep Learning for Engineers

Using Python and Google Cloud Platform
Erscheinungsjahr 2020
ISBN: 978-3-031-79664-7
Verlag: Springer International Publishing

Buch, Englisch, 93 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 226 g

Reihe: Synthesis Lectures on Mechanical Engineering

This book provides a short introduction and easy-to-follow implementation steps of deep learning using Google Cloud Platform. It also includes a practical case study that highlights the utilization of Python and related libraries for running a pre-trained deep learning model.

In recent years, deep learning-based modeling approaches have been used in a wide variety of engineering domains, such as autonomous cars, intelligent robotics, computer vision, natural language processing, and bioinformatics. Also, numerous real-world engineering applications utilize an existing pre-trained deep learning model that has already been developed and optimized for a related task. However, incorporating a deep learning model in a research project is quite challenging, especially for someone who doesn't have related machine learning and cloud computing knowledge. Keeping that in mind, this book is intended to be a short introduction of deep learning basics through the example of a practical implementation case.

The audience of this short book is undergraduate engineering students who wish to explore deep learning models in their class project or senior design project without having a full journey through the machine learning theories. The case study part at the end also provides a cost-effective and step-by-step approach that can be replicated by others easily.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Arif, Tariq M.

Fachgebiete

• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik
• Technische Wissenschaften Technik Allgemein Technik: Allgemeines
• Technische Wissenschaften Technik Allgemein Konstruktionslehre und -technik

Inhaltsverzeichnis

Preface.- Acknowledgments.- Introduction: Python and Array Operations.- Introduction to PyTorch.- Introduction to Deep Learning.- Deep Transfer Learning.- Case Study: Practical Implementation Through Transfer Learning.- Bibliography.- Author's Biography.

Über Autor(innen)

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. Tariq obtained his Ph.D. in 2017 from the Mechanical Engineering department of the New Jersey Institute of Technology (NJIT), NJ. His main research interests are in the area of artificial intelligence and genetic algorithm for robotics control, computer vision, and biomedical simulations of focused ultrasound surgery. He completed his Masters in 2011 from the University of Tokushima, Japan, and a B.Sc. in2005 from Bangladesh University of Engineering and Technology (BUET). Tariq also worked in the Japanese automobile industry as a CAD/CAE engineer after completing his B.Sc. degree. In his industrial and academic carrier, Tariq has been involved in many different research projects. Currently, he is working on the implementation of deep learning models for various engineering tasks.