Introduction to Deep Learning for Engineers
Using Python and Google Cloud Platform
- English
- PDF
- Available on iOS & Android
About this book
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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Information
Table of contents
- Cover
- Copyright Page
- Title Page
- 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’s Biography