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Introduction to Deep Learning for Engineers

SYNTHESIS LECT MECH ENG: INTRO DEEP LEARNING ENGINEERS

Information Technology

Computing & Information Technology

Gt Product Group

Binding Format

Marketing Imprint Name

B2B GT

CL Carton Count

Imprint Name

B2B & B2C

<P>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.</P>

Duration

Duration Unit Of Measure

Transaction Type

Front Cover.
Half Title Page.
Synthesis Lectures on Mechanical Engineering.
Copyright Page.
Title Page.
Other Frontmatter.
Table of Contents.
Preface.
Acknowledgments.
1: Introduction: Python and Array Operations.
2: Introduction to PyTorch.
3: Basic Artificial Neural Network and Architectures.
4: Introduction to Deep Learning.
5: Deep Transfer Learning.
6: Setting Up PyTorch and Google Cloud Platform Console.
7: Case Study: Practical Implementation Through Transfer Learning.
Bibliography.
Author&#x2019;s Biography.

Introduction to Deep Learning for Engineers.css-1p8rvzy{position:absolute;padding-left:8px;}

Introduction to Deep Learning for Engineers