Arif, Tariq M.
Introduction to Deep Learning for Engineers Using Python and Google Cloud Platform / [electronic resource] : by Tariq M. Arif. - 1st ed. 2020. - XV, 93 p. online resource. - Synthesis Lectures on Mechanical Engineering, 2573-3176 . - Synthesis Lectures on Mechanical Engineering, .
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 .
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.
9783031796654
10.1007/978-3-031-79665-4 doi
Engineering.
Electrical engineering.
Engineering design.
Microtechnology.
Microelectromechanical systems.
Technology and Engineering.
Electrical and Electronic Engineering.
Engineering Design.
Microsystems and MEMS.
T1-995
620
Introduction to Deep Learning for Engineers Using Python and Google Cloud Platform / [electronic resource] : by Tariq M. Arif. - 1st ed. 2020. - XV, 93 p. online resource. - Synthesis Lectures on Mechanical Engineering, 2573-3176 . - Synthesis Lectures on Mechanical Engineering, .
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 .
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.
9783031796654
10.1007/978-3-031-79665-4 doi
Engineering.
Electrical engineering.
Engineering design.
Microtechnology.
Microelectromechanical systems.
Technology and Engineering.
Electrical and Electronic Engineering.
Engineering Design.
Microsystems and MEMS.
T1-995
620