Introduction to Deep Learning for Engineers
Language: English
Published by Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2020
Series: Book 21 of 42 - Synthesis Lectures on Mechanical Engineering
- Softcover
- New
Condition: New
£ 28.54
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Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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.
Seller Inventory # 608129976
- Title
- Introduction to Deep Learning for Engineers
- Author
- Arif, Tariq M.
- Publisher
- Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer
- Publication year
- 2020
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 3031796640
- ISBN 13
- 9783031796647
- Series
- Book 21 of 42: Synthesis Lectures on Mechanical Engineering
- Seller catalogs
- Mathematik/Naturwissenschaften/Technik/Medizin
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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About the Author
"About the title" may belong to another edition of this title.
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