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Introduction to Deep Learning for Engineers [electronic resource] : Using Python and Google Cloud Platform / by Tariq M. Arif.

By: Arif, Tariq M [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Mechanical Engineering: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2020Edition: 1st ed. 2020.Description: XV, 93 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031796654.Subject(s): Engineering | Electrical engineering | Engineering design | Microtechnology | Microelectromechanical systems | Technology and Engineering | Electrical and Electronic Engineering | Engineering Design | Microsystems and MEMSAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 620 Online resources: Click here to access online
Contents:
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 .
In: Springer Nature eBookSummary: 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.
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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.

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