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You're reading from Hands-On Deep Learning for IoT Train neural network models to develop intelligent IoT applications

Product type Paperback

Published in Jun 2019

Publisher Packt

ISBN-13 9781789616132

Length 308 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Deep Learning

Authors (3):

Aditya Trivedi

Md. Rezaul Karim

Dr. Mohammad Abdur Razzaque

View More author details

Table of Contents (15) Chapters

Preface

1. Section 1: IoT Ecosystems, Deep Learning Techniques, and Frameworks FREE CHAPTER

2. The End-to-End Life Cycle of the IoT

3. Deep Learning Architectures for IoT

4. Section 2: Hands-On Deep Learning Application Development for IoT

5. Image Recognition in IoT

6. Audio/Speech/Voice Recognition in IoT

7. Indoor Localization in IoT

8. Physiological and Psychological State Detection in IoT

9. IoT Security

10. Section 3: Advanced Aspects and Analytics in IoT

11. Predictive Maintenance for IoT

12. Deep Learning in Healthcare IoT

13. What's Next - Wrapping Up and Future Directions

14. Other Books You May Enjoy

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Summary

In this chapter, we have discussed how indoor localization works for IoT enabled devices. In particular, we have seen how DL techniques can be used for indoor localization in IoT applications employing that data in general with a hands-on example. Furthermore, we have looked at some deployment settings of indoor localization services in IoT environments.

In Chapter 6, Physiological and Psychological State Detection in IoT, we will discuss DL-based human physiological and psychological state detection techniques for IoT applications in general. Considering a real-world scenario, we will look at two IoT applications based on physiological and psychological state detection.

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Authors (2)

Dr. Mohammad Abdur Razzaque

Dr. Mohammad Abdur Razzaque is a senior lecturer in the School of Computing and Digital Technologies, Teesside University, UK. He has more than 14 years of research and development and teaching experience on distributed systems (Internet of Things, P2P networking, and cloud computing) as well as experience in cybersecurity. He is an expert in end-to-end (sensors-to-cloud) IoT solutions. He offers consultancy in the areas of IoT solutions and the use of machine learning techniques in businesses. He has successfully published more than 65 research papers in these areas. He holds a PhD in distributed systems (P2P wireless sensor networks, mobile ad hoc networks) from the School of Computer Science and Informatics, UCD, Dublin (2008).

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Md. Rezaul Karim

Md. Rezaul Karim is a researcher, author, and data science enthusiast with a strong computer science background, coupled with 10 years of research and development experience in machine learning, deep learning, and data mining algorithms to solve emerging bioinformatics research problems by making them explainable. He is passionate about applied machine learning, knowledge graphs, and explainable artificial intelligence (XAI). Currently, he is working as a research scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Germany. Before joining FIT, he worked as a researcher at the Insight Centre for Data Analytics, Ireland. Previously, he worked as a lead software engineer at Samsung Electronics, Korea.

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