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Hands-On Deep Learning for IoT

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

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Product type Paperback
Published in Jun 2019
Publisher Packt
ISBN-13 9781789616132
Length 308 pages
Edition 1st Edition
Languages
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Authors (3):
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Aditya Trivedi Aditya Trivedi
Author Profile Icon Aditya Trivedi
Aditya Trivedi
Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
Dr. Mohammad Abdur Razzaque Dr. Mohammad Abdur Razzaque
Author Profile Icon Dr. Mohammad Abdur Razzaque
Dr. Mohammad Abdur Razzaque
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Toc

Table of Contents (15) Chapters Close

Preface 1. Section 1: IoT Ecosystems, Deep Learning Techniques, and Frameworks
2. The End-to-End Life Cycle of the IoT FREE CHAPTER 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

Image Recognition in IoT

Many IoT applications, including smart homes, smart cities, and smart healthcare, will extensively use image recognition-based decision-making (such as facial recognition for a smart door or lock) in the future. Machine learning (ML) and deep learning (DL) algorithms are useful for image recognition and decision-making. Consequently, they are very promising for IoT applications. This chapter will cover hands-on DL-based image data processing for IoT applications.

The first part of this chapter will briefly describe different IoT applications and their image detection-based decision-making. Furthermore, it will briefly discuss an IoT application and its image detection-based implementation in a real-world scenario. In the second part of the chapter, we shall present a hands-on image detection implementation of an application using a DL algorithm. In this...

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