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Introduction to Deep Learning for Engineers: Using Python and Google Cloud Platform (Synthesis Lectures on Mechanical Engineerin

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Introduction to Deep Learning for Engineers: Using Python and Google Cloud Platform (Synthesis Lectures on Mechanical Engineerin

This book provides a short introduction and easytofollow 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 pretrained deep learning model.In recent years, deep learningbased 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 realworld engineering applications utilize an existing pretrained 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 doesnt 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 costeffective and stepbystep approach that can be replicated by others easily.

$8.65

Original: $28.83

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Introduction to Deep Learning for Engineers: Using Python and Google Cloud Platform (Synthesis Lectures on Mechanical Engineerin

$28.83

$8.65

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This book provides a short introduction and easytofollow 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 pretrained deep learning model.In recent years, deep learningbased 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 realworld engineering applications utilize an existing pretrained 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 doesnt 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 costeffective and stepbystep approach that can be replicated by others easily.

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